scholarly journals Joint Cloud Water Path and Rain Water Path Retrievals from ORACLES Observations

2020 ◽  
Author(s):  
Andrew M. Dzambo ◽  
Tristan L'Ecuyer ◽  
Kenneth Sinclair ◽  
Bastiaan van Diedenhoven ◽  
Siddhant Gupta ◽  
...  
Keyword(s):  
Optical Depth ◽  
Liquid Water ◽  
Cloud Water ◽  
Retrieval Algorithm ◽  
Liquid Water Path ◽  
Cloud Optical Depth ◽  
Water Path ◽  
Surface Precipitation ◽  
Uncertainty Estimates ◽  
Cloud Water Path

Abstract. This study presents a new algorithm that combines W-band reflectivity measurements from the Airborne Precipitation Radar-3rd generation (APR-3), passive radiometric cloud optical depth and effective radius retrievals from the Research Scanning Polarimeter (RSP) to estimate total liquid water path in warm clouds and identify the contributions from cloud water path (CWP) and rainwater path (RWP). The resulting CWP estimates are primarily determined by the optical depth input, although reflectivity measurements contribute ~ 10–50 % of the uncertainty due to attenuation through the profile. Uncertainties in CWP estimates across all conditions are 25 % to 35 %, while RWP uncertainty estimates frequently exceed 100 %. Two thirds of all radar-detected clouds observed during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign that took place from 2016–2018 over the southeast Atlantic Ocean have CWP between 41 and 168 g m−2 and almost all CWPs (99 %) between 6 to 445 g m−2. RWP, by contrast, typically makes up a much smaller fraction of total liquid water path (LWP) with more than 70 % of raining clouds having less than 10 g m−2 of rainwater. In heavier warm rain (i.e. rain rate exceeding 40 mm h−1 or 1000 mm d−1), however, RWP is observed to exceed 2500 g m−2. CWP (RWP) is found to be approximately 30 g m−2 (7 g m−2) larger in unstable environments compared to stable environments. Surface precipitation is also more than twice as likely in unstable environments. Comparisons against in-situ cloud microphysical probe data spanning the range of thermodynamic stability and meteorological conditions encountered across the southeast Atlantic basin demonstrate that the combined APR-3 and RSP dataset enable a robust joint cloud-precipitation retrieval algorithm to support future ORACLES precipitation susceptibility and cloud–aerosol–precipitation interaction studies.

scholarly journals Joint cloud water path and rainwater path retrievals from airborne ORACLES observations

2021 ◽  
Vol 21 (7) ◽  
pp. 5513-5532
Author(s):  
Andrew M. Dzambo ◽  
Tristan L'Ecuyer ◽  
Kenneth Sinclair ◽  
Bastiaan van Diedenhoven ◽  
Siddhant Gupta ◽  
...  
Keyword(s):  
Optical Depth ◽  
Liquid Water ◽  
Cloud Water ◽  
Retrieval Algorithm ◽  
Liquid Water Path ◽  
Cloud Optical Depth ◽  
Water Path ◽  
Surface Precipitation ◽  
Uncertainty Estimates ◽  
Cloud Water Path

Abstract. This study presents a new algorithm that combines W-band reflectivity measurements from the Airborne Precipitation Radar – third generation (APR-3) passive radiometric cloud optical depth and effective radius retrievals from the Research Scanning Polarimeter (RSP) to estimate total liquid water path in warm clouds and identify the contributions from cloud water path (CWP) and rainwater path (RWP). The resulting CWP estimates are primarily determined by the optical depth input, although reflectivity measurements contribute ∼10 %–50 % of the uncertainty due to attenuation through the profile. Uncertainties in CWP estimates across all conditions are 25 % to 35 %, while RWP uncertainty estimates frequently exceed 100 %. Two-thirds of all radar-detected clouds observed during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign that took place from 2016–2018 over the southeast Atlantic Ocean have CWP between 41 and 168 g m−2 and almost all CWPs (99 %) between 6 to 445 g m−2. RWP, by contrast, typically makes up a much smaller fraction of total liquid water path (LWP), with more than 70 % of raining clouds having less than 10 g m−2 of rainwater. In heavier warm rain (i.e., rain rate exceeding 40 mm h−1 or 1000 mm d−1), however, RWP is observed to exceed 2500 g m−2. CWP (RWP) is found to be approximately 30 g m−2 (7 g m−2) larger in unstable environments compared to stable environments. Surface precipitation is also more than twice as likely in unstable environments. Comparisons against in situ cloud microphysical probe data spanning the range of thermodynamic stability and meteorological conditions encountered across the southeast Atlantic basin demonstrate that the combined APR-3 and RSP dataset enable a robust joint cloud–precipitation retrieval algorithm to support future ORACLES precipitation susceptibility and cloud–aerosol–precipitation interaction studies.

scholarly journals FAME-C: cloud property retrieval using synergistic AATSR and MERIS observations

2014 ◽  
Vol 7 (5) ◽  
pp. 4909-4947 ◽  
Author(s):  
C. K. Carbajal Henken ◽  
R. Lindstrot ◽  
R. Preusker ◽  
J. Fischer
Keyword(s):  
Physical Properties ◽  
Optical Thickness ◽  
Cloud Water ◽  
Effective Radius ◽  
Retrieval Algorithm ◽  
Water Path ◽  
Cloud Property ◽  
Uncertainty Estimates ◽  
Cloud Optical Thickness ◽  
Cloud Water Path

Abstract. A newly developed daytime cloud property retrieval algorithm FAME-C (Freie Universität Berlin AATSR MERIS Cloud) is presented. Synergistic observations from AATSR and MERIS, both mounted on the polar orbiting satellite ENVISAT, are used for cloud screening. For cloudy pixels two main steps are carried out in a sequential form. First, a micro-physical cloud property retrieval is performed using an AATSR near-infrared and visible channel. Cloud phase, cloud optical thickness, and effective radius are retrieved, and subsequently cloud water path is computed. Second, two independent cloud top height products are retrieved. For cloud top temperature AATSR brightness temperatures are used, while for cloud top pressure the MERIS oxygen-A absorption channel is used. Results from the micro-physical retrieval serve as input for the two cloud top height retrievals. Introduced are the AATSR and MERIS forward models and auxiliary data needed in FAME-C. Also, the optimal estimation method with uncertainty estimates, which also provides for uncertainty estimated of the retrieved property on a pixel-basis, is presented. Within the frame of the ESA Climate Change Initiative project first global cloud property retrievals have been conducted for the years 2007–2009. For this time period verification efforts are presented comparing FAME-C cloud micro-physical properties to MODIS-TERRA derived cloud micro-physical properties for four selected regions on the globe. The results show reasonable accuracies between the cloud micro-physical retrievals. Biases are generally smallest for marine stratocumulus clouds; −0.28, 0.41μm and −0.18 g m−2 for cloud optical thickness, effective radius and cloud water path, respectively. This is also true for the root mean square error. Also, both cloud top height products are compared to cloud top heights derived from ground-based cloud radars located at several ARM sites. FAME-C mostly shows an underestimation of cloud top heights when compared to radar observations, which is partly attributed to the difficulty of accurate cloud property retrievals for optically thin clouds and multi-layer clouds. The bias is smallest, −0.9 km, for AATSR derived cloud top heights for single-layer clouds.

Design of a Shadowband Spectral Radiometer for the Retrieval of Thin Cloud Optical Depth, Liquid Water Path, and the Effective Radius

2011 ◽  
Vol 28 (11) ◽  
pp. 1458-1465 ◽  
Author(s):  
M. J. Bartholomew ◽  
R. M. Reynolds ◽  
A. M. Vogelmann ◽  
Q. Min ◽  
R. Edwards ◽  
...  
Keyword(s):  
Optical Depth ◽  
Liquid Water ◽  
Effective Radius ◽  
Liquid Water Path ◽  
Cloud Optical Depth ◽  
Water Path ◽  
Cloud Properties ◽  
Radiative Intensity ◽  
Thin Cloud ◽  
Aerosol Properties

Abstract The design and operation of a Thin-Cloud Rotating Shadowband Radiometer (TCRSR) described here was used to measure the radiative intensity of the solar aureole and enable the simultaneous retrieval of cloud optical depth, drop effective radius, and liquid water path. The instrument consists of photodiode sensors positioned beneath two narrow metal bands that occult the sun by moving alternately from horizon to horizon. Measurements from the narrowband 415-nm channel were used to demonstrate a retrieval of the cloud properties of interest. With the proven operation of the relatively inexpensive TCRSR instrument, its usefulness for retrieving aerosol properties under cloud-free skies and for ship-based observations is discussed.

Understanding the Extratropical Liquid Water Path Feedback in Mixed-Phase Clouds with an Idealized Global Climate Model

2022 ◽  
pp. 1-48
Author(s):  
Yi Ming
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Climate Model ◽  
Global Climate Model ◽  
Cloud Water ◽  
Mixed Phase ◽  
Liquid Water Path ◽  
Water Path ◽  
Cloud Water Content ◽  
Mixed Phase Clouds

Abstract A negative shortwave cloud feedback associated with higher extratropical liquid water content in mixed-phase clouds is a common feature of global warming simulations, and multiple mechanisms have been hypothesized. A set of process-level experiments performed with an idealized global climate model (a dynamical core with passive water and cloud tracers and full Rotstayn-Klein single-moment microphysics) show that the common picture of the liquid water path (LWP) feedback in mixed-phase clouds being controlled by the amount of ice susceptible to phase change is not robust. Dynamic condensate processes—rather than static phase partitioning—directly change with warming, with varied impacts on liquid and ice amounts. Here, three principal mechanisms are responsible for the LWP response, namely higher adiabatic cloud water content, weaker liquid-to-ice conversion through the Bergeron-Findeisen process, and faster melting of ice and snow to rain. Only melting is accompanied by a substantial loss of ice, while the adiabatic cloud water content increase gives rise to a net increase in ice water path (IWP) such that total cloud water also increases without an accompanying decrease in precipitation efficiency. Perturbed parameter experiments with a wide range of climatological LWP and IWP demonstrate a strong dependence of the LWP feedback on the climatological LWP and independence from the climatological IWP and supercooled liquid fraction. This idealized setup allows for a clean isolation of mechanisms and paints a more nuanced picture of the extratropical mixed-phase cloud water feedback than simple phase change.

scholarly journals Estimation of liquid water path below the melting layer in stratiform precipitation systems using radar measurements during MC3E

2019 ◽  
Vol 12 (7) ◽  
pp. 3743-3759 ◽  
Author(s):  
Jingjing Tian ◽  
Xiquan Dong ◽  
Baike Xi ◽  
Christopher R. Williams ◽  
Peng Wu
Keyword(s):  
Liquid Water ◽  
Department Of Energy ◽  
Cloud Base ◽  
Retrieval Algorithm ◽  
Doppler Velocity ◽  
Liquid Water Path ◽  
Water Path ◽  
Melting Layer ◽  
Stratiform Precipitation ◽  
Cloud Particles

Abstract. In this study, the liquid water path (LWP) below the melting layer in stratiform precipitation systems is retrieved, which is a combination of rain liquid water path (RLWP) and cloud liquid water path (CLWP). The retrieval algorithm uses measurements from the vertically pointing radars (VPRs) at 35 and 3 GHz operated by the US Department of Energy Atmospheric Radiation Measurement (ARM) and National Oceanic and Atmospheric Administration (NOAA) during the field campaign Midlatitude Continental Convective Clouds Experiment (MC3E). The measured radar reflectivity and mean Doppler velocity from both VPRs and spectrum width from the 35 GHz radar are utilized. With the aid of the cloud base detected by a ceilometer, the LWP in the liquid layer is retrieved under two different situations: (I) no cloud exists below the melting base, and (II) cloud exists below the melting base. In (I), LWP is primarily contributed from raindrops only, i.e., RLWP, which is estimated by analyzing the Doppler velocity differences between two VPRs. In (II), cloud particles and raindrops coexist below the melting base. The CLWP is estimated using a modified attenuation-based algorithm. Two stratiform precipitation cases (20 and 11 May 2011) during MC3E are illustrated for two situations, respectively. With a total of 13 h of samples during MC3E, statistical results show that the occurrence of cloud particles below the melting base is low (9 %); however, the mean CLWP value can be up to 0.56 kg m−2, which is much larger than the RLWP (0.10 kg m−2). When only raindrops exist below the melting base, the average RLWP value is larger (0.32 kg m−2) than the with-cloud situation. The overall mean LWP below the melting base is 0.34 kg m−2 for stratiform systems during MC3E.

scholarly journals Parameterizing cloud top effective radii from satellite retrieved values, accounting for vertical photon transport: quantification and correction of the resulting bias in droplet concentration and liquid water path retrievals

2018 ◽  
Vol 11 (7) ◽  
pp. 4273-4289 ◽  
Author(s):  
Daniel P. Grosvenor ◽  
Odran Sourdeval ◽  
Robert Wood
Keyword(s):  
Penetration Depth ◽  
Optical Depth ◽  
Liquid Water ◽  
Liquid Water Content ◽  
Cloud Base ◽  
Percentage Error ◽  
Time Variability ◽  
Liquid Water Path ◽  
Water Path ◽  
Droplet Concentration

Abstract. Droplet concentration (Nd) and liquid water path (LWP) retrievals from passive satellite retrievals of cloud optical depth (τ) and effective radius (re) usually assume the model of an idealized cloud in which the liquid water content (LWC) increases linearly between cloud base and cloud top (i.e. at a fixed fraction of the adiabatic LWC). Generally it is assumed that the retrieved re value is that at the top of the cloud. In reality, barring re retrieval biases due to cloud heterogeneity, the retrieved re is representative of smaller values that occur lower down in the cloud due to the vertical penetration of photons at the shortwave-infrared wavelengths used to retrieve re. This inconsistency will cause an overestimate of Nd and an underestimate of LWP (referred to here as the “penetration depth bias”), which this paper quantifies via a parameterization of the cloud top re as a function of the retrieved re and τ. Here we estimate the relative re underestimate for a range of idealized modelled adiabatic clouds using bispectral retrievals and plane-parallel radiative transfer. We find a tight relationship between gre=recloud top/reretrieved and τ and that a 1-D relationship approximates the modelled data well. Using this relationship we find that gre values and hence Nd and LWP biases are higher for the 2.1 µm channel re retrieval (re2.1) compared to the 3.7 µm one (re3.7). The theoretical bias in the retrieved Nd is very large for optically thin clouds, but rapidly reduces as cloud thickness increases. However, it remains above 20 % for τ<19.8 and τ<7.7 for re2.1 and re3.7, respectively. We also provide a parameterization of penetration depth in terms of the optical depth below cloud top (dτ) for which the retrieved re is likely to be representative. The magnitude of the Nd and LWP biases for climatological data sets is estimated globally using 1 year of daily MODIS (MODerate Imaging Spectroradiometer) data. Screening criteria are applied that are consistent with those required to help ensure accurate Nd and LWP retrievals. The results show that the SE Atlantic, SE Pacific and Californian stratocumulus regions produce fairly large overestimates due to the penetration depth bias with mean biases of 32–35 % for re2.1 and 15–17 % for re3.7. For the other stratocumulus regions examined the errors are smaller (24–28 % for re2.1 and 10–12 % for re3.7). Significant time variability in the percentage errors is also found with regional mean standard deviations of 19–37 % of the regional mean percentage error for re2.1 and 32–56 % for re3.7. This shows that it is important to apply a daily correction to Nd for the penetration depth error rather than a time–mean correction when examining daily data. We also examine the seasonal variation of the bias and find that the biases in the SE Atlantic, SE Pacific and Californian stratocumulus regions exhibit the most seasonality, with the largest errors occurring in the December, January and February (DJF) season. LWP biases are smaller in magnitude than those for Nd (−8 to −11 % for re2.1 and −3.6 to −6.1 % for re3.7). In reality, and especially for more heterogeneous clouds, the vertical penetration error will be combined with a number of other errors that affect both the re and τ, which are potentially larger and may compensate or enhance the bias due to vertical penetration depth. Therefore caution is required when applying the bias corrections; we suggest that they are only used for more homogeneous clouds.

scholarly journals Cloud Liquid Water Path from Satellite-Based Passive Microwave Observations: A New Climatology over the Global Oceans

2008 ◽  
Vol 21 (8) ◽  
pp. 1721-1739 ◽  
Author(s):  
Christopher W. O’Dell ◽  
Frank J. Wentz ◽  
Ralf Bennartz
Keyword(s):  
Liquid Water ◽  
Systematic Errors ◽  
Cloud Water ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Water Path ◽  
Microwave Imager ◽  
Cloud Liquid Water Path ◽  
The Tropics ◽  
Global Oceans

Abstract This work describes a new climatology of cloud liquid water path (LWP), termed the University of Wisconsin (UWisc) climatology, derived from 18 yr of satellite-based passive microwave observations over the global oceans. The climatology is based on a modern retrieval methodology applied consistently to the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS) (AMSR-E) microwave sensors on eight different satellite platforms, beginning in 1988 and continuing through 2005. It goes beyond previously published climatologies by explicitly solving for the diurnal cycle of cloud liquid water by providing statistical error estimates, and includes a detailed discussion of possible systematic errors. A novel methodology for constructing the climatology is used in which a mean monthly diurnal cycle as well as monthly means of the liquid water path are derived simultaneously from the data on a 1° grid; the methodology also produces statistical errors for these quantities, which decrease toward the end of the time record as the number of observations increases. The derived diurnal cycles are consistent with previous findings in the tropics, but are also derived for higher latitudes and contain more information than in previous studies. The new climatology exhibits differences with previous observationally based climatologies and is found to be more consistent with the 40-yr ECMWF Re-Analysis (ERA-40) than are the previous climatologies. Potential systematic errors of the order of 15%–30% or higher exist in the LWP climatology. A previously unexplored source of systematic error is caused by the assumption that all microwave-based retrievals of LWP must make regarding the partitioning of cloud water and rainwater, which cannot be determined using microwave observations alone. The potentially large systematic errors that result may hamper the usefulness of microwave-based climatologies of both cloud liquid water and especially rain rate, particularly in certain regions of the tropics and midlatitudes where the separation of rain from liquid cloud water is most critical.

Comparison of in situ cirrus cloud water path and optical depth measurements with MODIS retrievals

2004 ◽  
Author(s):  
Gerald G. Mace ◽  
Ryan Riveland ◽  
Sally Benson ◽  
Steven E. Platnick ◽  
Linnea Avallone ◽  
...  
Keyword(s):  
Optical Depth ◽  
Cloud Water ◽  
Cirrus Cloud ◽  
Water Path ◽  
Cloud Water Path
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