In this study, Gaussian mixture model clustering analysis was carried out to examine characteristics of Global Precipitation Measurement (GPM) Dual‐frequency Precipitation Radar (DPR)‐retrieved ...mass‐weighted mean diameter (Dm), and normalized intercept parameter (Nw) of the drop size distribution (DSD) for heavy rainfalls (>10 mm h−1) for 6 years (2014–2019). Three objective DSD types – continental, oceanic deep, and oceanic shallow convective types – emerged. The means and standard deviations of Dm and Nw obtained for the three types are in good agreement with various ground‐based observations, indicating that global view of DSD characteristics can be obtained from DPR‐derived DSD parameters. Global distributions of occurrence and contribution of each DSD type to total heavy rainfall are produced for the first time, which will help examine the dominant DSD type, its contribution to total heavy rainfall, and composition of different convective types in the rainfall system at a given location.
Plain Language Summary
The surface rainfall is composed of a variety of spectrum of raindrops, which can be best represented by mean drop size and number concentration of droplets. Thus, those magnitude and shape may well describe rainfall‐related features such as convective type and associated atmospheric environments. Thus, information on the rain drop size distribution is important for improving the remote sensing capability or modeling the rainfall phenomena. From the analysis of satellite‐derived rain drop size distribution, it is noted that the heavy rainfall can be largely classified into three types – continental, oceanic deep, and oceanic shallow convective types. Satellite‐derived mean diameter and drop size distribution for heavy rain are found to be very consistent with ground observations from limited local areas, indicating that the global view of drop size distributions can be synthesized from the satellite observations. The newly obtained global features overcome the spatial limitations of existing studies using ground‐based observations. Furthermore, estimated contribution to the heavy rainfall from each classified type shows that a largest portion is from the oceanic deep convective type, and the oceanic shallow convective type contributes as much as the continental type.
Key Points
Global synthesis of drop size distributions for heavy rainfall using satellite‐borne radar measurements
Three heavy rainfall types emerged – continental, oceanic deep, and oceanic shallow convective types
Geographic distributions of occurrence frequencies and rain contributions of three types are presented
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Current satellite cloud products from passive radiometers provide effective single‐layer cloud properties by assuming a homogeneous cloud in a pixel, resulting in inevitable biases when ...multiple‐layer clouds are present in a vertical column. We devise a novel method to retrieve cloud vertical properties for ice‐over‐water clouds using passive radiometers. Based on the absorptivity differences of liquid water and ice clouds at four shortwave‐infrared channels (centered at 0.87, 1.61, 2.13, and 2.25 μm), cloud optical thicknesses (COT) and effective radii of both upper‐layer ice and lower‐layer liquid water clouds are inferred simultaneously. The algorithm works most effectively for clouds with ice COT < 7 and liquid water COT > 5. The simulated spectral reflectances based on our retrieved ice‐over‐water clouds become more consistent with observations than those with a single‐layer assumption. This new algorithm will improve our understanding of clouds, and we suggest that these four cloud channels should be all included in future satellite sensors.
Plain Language Summary
Over a quarter of clouds in the atmosphere overlap in a vertical column, and ignoring the cloud vertical distribution may significantly influence estimation of their radiative effects. However, information about cloud vertical structures is mostly provided by in situ or active instruments with limited spatiotemporal resolution. Cloud properties from satellite passive radiometer observations are derived by treating a cloud pixel as a single‐layer cloud, resulting in biases in our understanding of clouds and their radiative forcing. This study improves the capabilities of passive radiometers for retrieving properties of ice‐over‐water clouds, including the optical thicknesses and effective radii of both upper ice and lower liquid water clouds simultaneously. Our method provides a new perspective for radiometer‐based retrieval of multilayer cloud properties and will improve the evaluation of cloud radiative effects.
Key Points
A cloud microphysical and optical property retrieval algorithm for ice‐over‐water clouds using passive satellite observations is developed
The absorptivity differences of water and ice at three shortwave‐infrared channels are used to differentiate between water and ice clouds
The retrieved cloud optical properties are found to be significantly improved, compared to MODIS‐derived properties
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Supercooled water clouds (SWCs) have significant impacts on the Earth's radiation balance, aircraft ice accretion and precipitation augmentation. This study introduces an efficient algorithm to ...detect SWCs from passive radiometers, which combines information from the reflectance difference between 1.61 and 2.25 μm channels, the brightness temperature difference between the 8.5 and 11 μm channels, and the cloud top temperature. Validated by space radar and lidar measurements, our algorithm can correctly detect 91% of SWC pixels, better than current Visible Infrared Imager Radiometer Suite operational product. SWCs are found mostly over the mid‐ to high‐latitude oceans and have a global occurrence frequency of ∼8% in cloudy skies. Since the channels used for the detection are available in most current operational polar and geostationary satellite radiometers, this SWC detection algorithm can be easily implemented for operations such as cloud monitoring, aviation safety, and SWC‐related weather modification.
Plain Language Summary
Supercooled water clouds (SWCs) are clouds with temperatures under the freezing point but still containing liquid water droplets. The detection of SWCs is crucial for aviation safety as aviation accidents are frequently caused by the accumulation of supercooled water droplets on airplanes. Detecting SWCs is also critical for artificial precipitation because the implementation is highly dependent on SWC contents. Moreover, SWCs are of great importance in the Earth's radiation budget. However, since SWCs from passive radiometer measurements are normally determined with empirical relationships of the channel radiance and cloud properties, the results are often subject to certain biases or required pre‐retrieved cloud properties. This study introduces a simple but efficient detection algorithm to identify SWCs on a global scale from the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Suomi‐National Polar‐orbiting Partnership (NPP) satellite. Our algorithm is able to detect over 90% occurrence of SWCs, which accounts for ∼8% among all cloudy sky, with most located over the mid‐ to high‐latitude oceans. Our algorithm can be used for geoscience researches, such as estimating the cloud radiative effect, and for applications such as aviation safety and weather modification operations.
Key Points
An efficient algorithm to detect supercooled water clouds (SWC) from passive radiometer measurements is developed
The algorithm correctly identifies 91% of SWCs determined by active sensors, significantly better than that in the Visible Infrared Imager Radiometer Suite (VIIRS) operational product
The passive radiometer VIIRS suggests a global SWC occurrence frequency of ∼8% in cloudy skies
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Polarized emissivities of the sea ice over the Arctic were retrieved at Advanced Microwave Scanning Radiometer–EOS 10.65, 18.7, 23.8, and 36.5 GHz channel frequencies. Results indicate that retrieved ...emissivities are consistent with other emissivity estimates. However, errors in the retrieved emissivity for multiyear sea ice at 23.8 and 36.5 GHz can be large up to 8% and 20%, respectively, because of ignoring the freeboard ice scattering and the use of the same emission layer as in 6.925 GHz. It is shown that the emissivity slope for first‐year ice between 10.65 and 18.7 GHz is opposite to that for multiyear sea ice, enabling a distinction between first‐year ice and multiyear ice. Using these differences in spectral features with ice types, an emissivity difference (vertically polarized emissivity difference between 10.65 and 18.7 GHz) was devised to differentiate between first‐year sea ice and multiyear sea ice. A comparison with the ice status information obtained from Cold Regions Research and Engineering Laboratory buoy measurements demonstrates that the method can separate first‐year ice from multiyear ice, implying that this technique enables us to obtain instantaneous and pixel‐level ice‐type information from space‐based passive microwave measurements.
Key Points
A new algorithm for retrieving sea ice emissivities at microwave frequencies
The spectral emissivity slope between 10.6 and 18.7 GHz was devised as a means of differentiating between first‐year and multiyear sea ice
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
We observe a notable decreasing trend of June rainfall over the Korean peninsula in recent 20 years. This drought condition is found to be linked to the polarizing trend of rainfall intensity; more ...non‐rain and drizzle‐like rain, less moderate‐intensity rain, and more heavy rain events. Overall, the June drought over the Korean peninsula is found to be associated with less occurring moderate‐intensity rain. This feature is interpreted as that the dominant warm‐type heavy rain systems with a medium storm height tends to be less frequent while cold‐type heavy rains characterized by taller storm become more frequent during last 20 years. The northwestward expansion of the North Pacific high in June appears to weaken the continuous moisture supply to the Korean peninsula, which is a main element of forming the warm‐type heavy rain there.
Climatological mean (left) and linear trend (right) for the frequency distribution of storm height normalized by near‐surface radar reflectivities over the Korean domain (June 1998–2017).
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using ...spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ∼5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ∼5%, while at the same time, reducing misjudgment by ∼3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This article introduces an international regional experiment, East Asian Regional Experiment 2005 (EAREX 2005), carried out in March–April 2005 in the east Asian region, as one of the first phase ...regional experiments under the UNEP Atmospheric Brown Cloud (ABC) project, and discusses some outstanding features of aerosol characteristics and its direct radiative forcing in the east Asian region, with some comparison with the results obtained in another ABC early phase regional experiment, ABC Maldives Monsoon Experiment (APMEX) conducted in the south Asian region. Time series of aerosol optical thickness (AOT), single scattering albedo (SSA), aerosol extinction cross section profile and CO concentration shows that air pollutants and mineral dust were transported every 5 to 7 days in the EAREX region to produce SSA values at wavelength of 700 nm from 0.86 to 0.96 and large clear‐sky shortwave forcing efficiency at 500 nm from 60 W m−2 to 90 W m−2, though there are some unexplained inconsistencies depending on the evaluation method. The simulated whole‐sky total forcing in the EAREX region is −1 to −2 W m−2 at TOA and −2 to −10 W m−2 at surface in March 2005 which is smaller in magnitude than in the APMEX region, mainly because of large cloud fraction in this region (0.70 at Gosan versus 0.51 at Hanimadhoo in the ISCCP total cloud fraction). We suggest there may be an underestimation of the forcing due to overestimation of the simulated cloudiness and aerosol scale height. On the other hand, the possible error in the simulated surface albedo may cause an overestimation of the magnitude of the forcing over the land area. We also propose simple formulae for shortwave radiative forcing to understand the role of aerosol parameters and surface condition to determine the aerosol forcing. Such simple formulae are useful to check the consistency among the observed quantities.
Recent satellite observations suggested that medium‐depth heavy rain systems (i.e., warm‐type heavy rainfall) were predominantly found in the Korean peninsula under moist‐adiabatically near neutral ...conditions in contrast to the traditional view that deep convection induced by convective instability produced heavy rainfall (i.e., cold‐type heavy rainfall). In order to examine whether a numerical model could explain the microphysical evolution of the warm‐type as well as cold‐type heavy rainfall, numerical experiments were implemented with idealized thermodynamic conditions. Under the prescribed humid and weakly unstable conditions, the warm‐type experiments resulted in a lower storm height, earlier onset of precipitation, and heavier precipitation than was found for the cold‐type experiments. The growth of ice particles and their melting process were important for developing cold‐type heavy rainfall. In contrast, the collision and coalescence processes between liquid particles were shown to be the mechanism for increasing the radar reflectivity toward the surface in the storm core region for the warm‐type heavy rainfall.
Key Points
Numerical experiments are performed with idealized thermodynamic conditions to understand the formation of “warm‐type” heavy rainfall
Under humid and moist‐adiabatically near neutral conditions, “warm‐type” clouds with a lower storm height can also produce heavy rainfall
The collision‐coalescence process of raindrops is responsible for producing the warm‐type heavy rainfall
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
A total of 10 years (2002-11) of Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) reflectivities, signaling heavy rainfall (>10 mm h super(-1)), were objectively classified by ...applying the K-means clustering method in order to obtain typical reflectivity profiles associated with heavy rainfall over East Asia. Two types of heavy rainfall emerged as the most important rain processes over East Asia: type 1 (cold type) characterized by high storm height and abundant ice water under convectively unstable conditions, developing mostly over inland China; and type 2 (warm type) associated with a lower storm height and lower ice water content, developing mostly over the ocean. These two types also show sharp contrasts in relation to their seasonal changes and in the diurnal variation of frequency maxima, in addition to other contrasting meteorological parameters. The PR-derived heavy rain events were observed over the Korean peninsula and their spatiotemporal evolution was examined using 10-yr composites of 11- mu m brightness temperature from geostationary satellites and Interim ECMWF Re-Analysis (ERA-Interim) data. Cold-type heavy rainfall over Korea is characterized by an eastward moving cloud system with an oval shape while the warm type shows a comparatively wide spatial distribution over an area extending from the southwest to northeast. Overall the warm-type process appears to link the low-level moisture convergence area to the vertically aligned divergence area formed over the jet stream level. This setup continuously pushes air upward under moist-adiabatically near-neutral conditions and thus yields heavy rainfall. As warm-type heavy rainfall persists longer, it is considered to be more responsible for flood events occurring over the Korean peninsula.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Data assimilation of satellite microwave measurements is one of the important keys to improving weather forecasting over the Arctic region. However, the use of surface‐sensitive microwave‐sounding ...channel measurements for data assimilation or retrieval has been limited, especially during winter, due to the poorly constrained sea ice emissivity. In this study, aiming at more use of those channel measurements in the data assimilation, we propose an explicit method for specifying the surface radiative boundary conditions (namely emissivity and emitting layer temperature of snow and ice). These were explicitly determined with a radiative transfer model for snow and ice and with snow/ice physical parameters (i.e. snow/ice depths and vertical distributions of temperature, density, salinity, and grain size) simulated from the thermodynamically driven snow/ice growth model. We conducted 1D‐Var experiments in order to examine whether this approach can help to use the surface‐sensitive microwave temperature channel measurements over the Arctic sea ice region for data assimilation. Results show that (1) the surface‐sensitive microwave channels can be used in the 1D‐Var retrieval, and (2) the specification of the radiative boundary condition at the surface using the snow/sea ice emission model can significantly improve the atmospheric temperature retrieval, especially in the lower troposphere (500 hPa to surface). The successful retrieval suggests that useful information can be extracted from surface‐sensitive microwave‐sounding channel radiances over sea ice surfaces through the explicit determination of snow/ice emissivity and emitting layer temperature.
This article proposes an explicit method for estimating multi‐spectral emissivities and associated emission temperatures for microwave window and temperature‐sounding channels in the wintertime Arctic sea ice region, with information and constraints from the thermodynamic growth model and radiative transfer model for snow and ice. Subsequently, through the application of this method along the trajectory of the MOSAiC expedition from 1 December 2019 to 31 March 2020, we successfully demonstrated that the explicit method is capable of describing the surface boundary conditions necessary for the atmospheric radiative transfer calculation. These results not only provide a solid foundation for microwave data assimilation but also open a door to the atmosphere–snow/ice–ocean coupled data assimilation.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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