scholarly journals Detection and characterization of drizzle cells within marine stratocumulus using AMSR-E 89 GHz passive microwave measurements

2012 ◽  
Vol 5 (4) ◽  
pp. 4571-4597
Author(s):  
M. A. Miller ◽  
S. E. Yuter
Keyword(s):  
Brightness Temperature ◽  
Liquid Water ◽  
Cell Number ◽  
Passive Microwave ◽  
Liquid Water Path ◽  
Marine Stratocumulus ◽  
Data Set ◽  
Local Maxima ◽  
Spatial Coverage ◽  
Background Brightness

Abstract. This empirical study demonstrates the feasibility of using 89 GHz Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) passive microwave brightness temperature data to detect heavily drizzling cells within marine stratocumulus. A binary heavy drizzle product is described that can be used to determine areal and feature statistics of drizzle cells within the major marine stratocumulus regions. Current satellite liquid water path (LWP) and cloud radar products capable of detecting drizzle are either lacking in resolution (AMSR-E LWP), diurnal coverage (MODIS LWP), or spatial coverage (CloudSat). The AMSR-E 89 GHz data set at 6 × 4 km spatial resolution is sufficient for resolving individual heavily drizzling cells. Radiant emission at 89 GHz by liquid-water cloud and precipitation particles from drizzling cells in marine stratocumulus regions yields local maxima in brightness temperature against an otherwise cloud-free background brightness temperature. The background brightness temperature is primarily constrained by column-integrated water vapor and sea surface temperature. Clouds containing ice are screened out. Once heavily drizzling pixels are identified, connected pixels are grouped into discrete drizzle cell features. The identified drizzle cells are used in turn to determine several spatial statistics for each satellite scene, including drizzle cell number and size distribution. The identification of heavily drizzling cells within marine stratocumulus regions with satellite data facilitates analysis of seasonal and regional drizzle cell occurrence and the interrelation between drizzle and changes in cloud fraction.

scholarly journals Detection and characterization of heavy drizzle cells within subtropical marine stratocumulus using AMSR-E 89-GHz passive microwave measurements

2013 ◽  
Vol 6 (1) ◽  
pp. 1-13 ◽  
Author(s):  
M. A. Miller ◽  
S. E. Yuter
Keyword(s):  
Brightness Temperature ◽  
Liquid Water ◽  
Cell Number ◽  
Passive Microwave ◽  
Liquid Water Path ◽  
Marine Stratocumulus ◽  
Data Set ◽  
Local Maxima ◽  
Spatial Coverage ◽  
Background Brightness

Abstract. This empirical study demonstrates the feasibility of using 89-GHz Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) passive microwave brightness temperature data to detect heavily drizzling cells within subtropical marine stratocumulus. For the purpose of this paper, we define heavily drizzling cells as areas ≥ 6 km × 4 km with C-band Z > 0 dBZ; equivalent to > 0.084 mm h−1. A binary heavy drizzle product is described that can be used to determine areal and feature statistics of drizzle cells within the major marine stratocumulus regions. Current satellite liquid water path (LWP) and cloud radar products capable of detecting drizzle are either lacking in resolution (AMSR-E LWP), diurnal coverage (MODIS LWP), or spatial coverage (CloudSat). The AMSR-E 89-GHz data set at 6 km × 4 km spatial resolution is sufficient for resolving individual heavily drizzling cells. Radiant emission at 89 GHz by liquid-water cloud and precipitation particles from drizzling cells in marine stratocumulus regions yields local maxima in brightness temperature against an otherwise cloud-free background brightness temperature. The background brightness temperature is primarily constrained by column-integrated water vapor for moderate sea surface temperatures. Clouds containing ice are screened out. Once heavily drizzling pixels are identified, connected pixels are grouped into discrete drizzle cell features. The identified drizzle cells are used in turn to determine several spatial statistics for each satellite scene, including drizzle cell number and size distribution. The identification of heavily drizzling cells within marine stratocumulus regions with satellite data facilitates analysis of seasonal and regional drizzle cell occurrence and the interrelation between drizzle and changes in cloud fraction.

scholarly journals Relationship between drizzle rate, liquid water path and droplet concentration at the scale of a stratocumulus cloud system

2008 ◽  
Vol 8 (16) ◽  
pp. 4641-4654 ◽  
Author(s):  
O. Geoffroy ◽  
J.-L. Brenguier ◽  
I. Sandu
Keyword(s):  
Liquid Water ◽  
Field Experiments ◽  
Liquid Water Path ◽  
Marine Stratocumulus ◽  
Data Set ◽  
Water Path ◽  
Droplet Concentration ◽  
Stratocumulus Clouds ◽  
Shallow Clouds ◽  
The One

Abstract. The recent ACE-2, EPIC and DYCOMS-II field experiments showed that the drizzle precipitation rate of marine stratocumulus scales with the cloud geometrical thickness or liquid water path, and the droplet concentration, when averaged over a domain typical of a GCM grid. This feature is replicated here with large-eddy-simulations using state-of-the-art bulk parameterizations of precipitation formation in stratocumulus clouds. The set of numerical simulations shows scaling relationships similar to the ones derived from the field experiments, especially the one derived from the DYCOMS-II data set. This result suggests that the empirical relationships were not fortuitous and that they reflect the mean effect of cloud physical processes. Such relationships might be more suited to GCM parameterizations of precipitation from shallow clouds than bulk parameterizations of autoconversion, that were initially developed for cloud resolving models.

scholarly journals Relationship between drizzle rate, liquid water path and droplet concentration at the scale of a stratocumulus cloud system

2008 ◽  
Vol 8 (1) ◽  
pp. 3921-3959 ◽  
Author(s):  
O. Geoffroy ◽  
J.-L. Brenguier ◽  
I. Sandu
Keyword(s):  
Liquid Water ◽  
Field Experiments ◽  
Liquid Water Path ◽  
Marine Stratocumulus ◽  
Data Set ◽  
Water Path ◽  
Droplet Concentration ◽  
Stratocumulus Clouds ◽  
Shallow Clouds ◽  
The One

Abstract. The recent ACE-2, EPIC and DYCOMS-II field experiments showed that the drizzle precipitation rate of marine stratocumulus scales with the cloud geometrical thickness or liquid water path, and the droplet concentration, when averaged over a domain typical of a GCM grid. This feature is replicated here with large-eddy-simulations using state-of-the-art bulk parameterizations of precipitation formation in stratocumulus clouds. The set of numerical simulations shows scaling relationships similar to the ones derived from the field experiments, especially the one derived from the DYCOMS-II data set. This result suggests that the empirical relationships were not fortuitous and that they reflect the mean effect of cloud physical processes. Such relationships might be more suited to GCM parameterizations of precipitation from shallow clouds than bulk parameterizations of autoconversion, that were initially developed for cloud resolving models.

scholarly journals Investigating the liquid water path over the tropical Atlantic with synergistic airborne measurements

2019 ◽  
Author(s):  
Marek Jacob ◽  
Felix Ament ◽  
Manuel Gutleben ◽  
Heike Konow ◽  
Mario Mech ◽  
...  
Keyword(s):  
Water Vapor ◽  
Liquid Water ◽  
Critical Role ◽  
Dry Season ◽  
Passive Microwave ◽  
Variable Component ◽  
Wet Season ◽  
Liquid Water Path ◽  
Water Path ◽  
Airborne Measurements

Abstract. Clouds are a strongly variable component of the climate system and several studies have identified especially marine low level clouds to play a critical role for the climate. Liquid water path (LWP) is an important quantity to characterize clouds. Passive microwave satellite sensors provide the most direct estimate on global scale, but suffer from high uncertainties due to large footprints and the superposition of cloud and precipitation signals. Here, we use high spatial resolution airborne microwave radiometer (MWR) measurements together with cloud radar and lidar observations to better understand LWP of warm clouds over the tropical North Atlantic. The nadir measurements were taken by the German High Altitude and Long range research aircraft (HALO) in December 2013 (dry season) and August 2016 (wet season) during two Next generation Advanced Remote sensing for VALidation campaigns (NARVAL). Microwave retrievals of integrated water vapor (IWV), LWP and rain water path (RWP) are developed using artificial neural network techniques and a unique database based on cloud-resolving model simulations with 1.25 km grid spacing. The IWV and LWP retrievals share the same eight MWR frequency channels as their sole input. The comparison of retrieved IWV with coincident dropsondes and water vapor lidar measurements shows root-mean-square deviations below 1.4 kg m−2 over the range from 20 to 60 kg m−2. This comparison raises the confidence in LWP retrievals which can only be assessed theoretically. The theoretical analysis shows the dependency of the uncertainty on LWP itself as the error is below 20 g m−2 for LWP below 100 g m−2 and below 20 % above. The identification of clear sky scenes by ancillary measurements, here backscatter lidar, is crucial for thin clouds (LWP < 12 g m−2) as the microwave retrieved LWP uncertainty is higher than 100 %. The RWP retrieval combines active and passive microwave observations and is able to detect drizzle and light precipitation. The analysis of both campaigns reveals that clouds were more frequent in the dry than in the wet season and their LWP and RWP were higher, but microwave scattering of ice was observed more frequently in the wet season (1.6 % vs. 0.5 % of the time). As to be expected, the observed IWV clearly shows that the wet season (mean IWV = 41 kg m−2) is more humid than the dry season (mean IWV = 28 kg m−2). The results reveal that the observed frequency distributions of IWV are strongly affected by the choice of the flight pattern. Therefore, the airborne observations need to be used carefully to mediate between long-term ground-based and spaceborne measurements to draw statistically sound conclusions.

scholarly journals Microwave signatures of ice hydrometeors from ground-based observations above Summit, Greenland

2015 ◽  
Vol 15 (23) ◽  
pp. 34497-34532
Author(s):  
C. Pettersen ◽  
R. Bennartz ◽  
M. S. Kulie ◽  
A. J. Merrelli ◽  
M. D. Shupe ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Temperature Difference ◽  
Liquid Water ◽  
High Frequency ◽  
Passive Microwave ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Cloud Radar ◽  
Brightness Temperatures ◽  
High Frequency Microwave

Abstract. Multi-instrument, ground-based measurements provide unique and comprehensive datasets of the atmosphere for a specific location over long periods of time and resulting data compliments past and existing global satellite observations. This paper explores the effect of ice hydrometeors on ground-based, high frequency passive microwave measurements and attempts to isolate an ice signature for summer seasons at Summit, Greenland from 2010–2013. Data from a combination of passive microwave, cloud radar, radiosonde, and ceilometer were examined to isolate the ice signature at microwave wavelengths. By limiting the study to a cloud liquid water path of 40 g m−2 or less, the cloud radar can identify cases where the precipitation was dominated by ice. These cases were examined using liquid water and gas microwave absorption models, and brightness temperatures were calculated for the high frequency microwave channels: 90, 150, and 225 GHz. By comparing the measured brightness temperatures from the microwave radiometers and the calculated brightness temperature using only gas and liquid contributions, any residual brightness temperature difference is due to emission and scattering of microwave radiation from the ice hydrometeors in the column. The ice signature in the 90, 150, and 225 GHz channels for the Summit Station summer months was isolated. This measured ice signature was then compared to an equivalent brightness temperature difference calculated with a radiative transfer model including microwave single scattering properties for several ice habits. Initial model results compare well against the four years of summer season isolated ice signature in the high-frequency microwave channels.

scholarly journals Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

2019 ◽  
Vol 11 (19) ◽  
pp. 2265 ◽  
Author(s):  
Yonghwan Kwon ◽  
Barton A. Forman ◽  
Jawairia A. Ahmad ◽  
Sujay V. Kumar ◽  
Yeosang Yoon
Keyword(s):  
Standard Deviation ◽  
Data Assimilation ◽  
Brightness Temperature ◽  
Liquid Water ◽  
Temperature Data ◽  
Passive Microwave ◽  
Support Vector ◽  
Surface Model ◽  
High Mountain ◽  
Microwave Brightness Temperature

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔTB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔTB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔTB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔTB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

scholarly journals Investigating the liquid water path over the tropical Atlantic with synergistic airborne measurements

2019 ◽  
Vol 12 (6) ◽  
pp. 3237-3254 ◽  
Author(s):  
Marek Jacob ◽  
Felix Ament ◽  
Manuel Gutleben ◽  
Heike Konow ◽  
Mario Mech ◽  
...  
Keyword(s):  
Water Vapor ◽  
Liquid Water ◽  
Dry Season ◽  
Passive Microwave ◽  
Wet Season ◽  
Liquid Water Path ◽  
Direct Estimate ◽  
Flight Pattern ◽  
Water Path ◽  
Airborne Measurements

Abstract. Liquid water path (LWP) is an important quantity to characterize clouds. Passive microwave satellite sensors provide the most direct estimate on a global scale but suffer from high uncertainties due to large footprints and the superposition of cloud and precipitation signals. Here, we use high spatial resolution airborne microwave radiometer (MWR) measurements together with cloud radar and lidar observations to better understand the LWP of warm clouds over the tropical North Atlantic. The nadir measurements were taken by the German High Altitude and LOng range research aircraft (HALO) in December 2013 (dry season) and August 2016 (wet season) during two Next-generation Advanced Remote sensing for VALidation (NARVAL) campaigns. Microwave retrievals of integrated water vapor (IWV), LWP, and rainwater path (RWP) are developed using artificial neural network techniques. A retrieval database is created using unique cloud-resolving simulations with 1.25 km grid spacing. The IWV and LWP retrievals share the same eight MWR frequency channels in the range from 22 to 31 GHz and at 90 GHz as their sole input. The RWP retrieval combines active and passive microwave observations and is able to detect drizzle and light precipitation. The comparison of retrieved IWV with coincident dropsondes and water vapor lidar measurements shows root-mean-square deviations below 1.4 kg m−2 over the range from 20 to 60 kg m−2. This comparison raises the confidence in LWP retrievals which can only be assessed theoretically. The theoretical analysis shows that the LWP error is constant with 20 g m−2 for LWP below 100 g m−2. While the absolute LWP error increases with increasing LWP, the relative one decreases from 20 % at 100 g m−2 to 10 % at 500 g m−2. The identification of clear-sky scenes by ancillary measurements, here backscatter lidar, is crucial for thin clouds (LWP < 12 g m−2) as the microwave retrieved LWP uncertainty is higher than 100 %. The analysis of both campaigns reveals that clouds were more frequent (47 % vs. 30 % of the time) in the dry than in the wet season. Their average LWP (63 vs. 40 g m−2) and RWP (6.7 vs. 2.7 g m−2) were higher as well. Microwave scattering of ice, however, was observed less frequently in the dry season (0.5 % vs. 1.6 % of the time). We hypothesize that a higher degree of cloud organization on larger scales in the wet season reduces the overall cloud cover and observed LWP. As to be expected, the observed IWV clearly shows that the dry season is on average less humid than the wet season (28 vs. 41 kg m−2). The results reveal that the observed frequency distributions of IWV are substantially affected by the choice of the flight pattern. This should be kept in mind when using the airborne observations to carefully mediate between long-term ground-based and spaceborne measurements to draw statistically sound conclusions.

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