scholarly journals Detecting the Ratio of Rain and Cloud Water in Low-Latitude Shallow Marine Clouds

2011 ◽  
Vol 50 (2) ◽  
pp. 419-432 ◽  
Author(s):  
Matthew D. Lebsock ◽  
Tristan S. L’Ecuyer ◽  
Graeme L. Stephens
Keyword(s):  
Optical Depth ◽  
Cloud Water ◽  
Low Latitude ◽  
Shallow Marine ◽  
Water Path ◽  
Precipitation Water ◽  
Low Cloud ◽  
Moderate Resolution ◽  
Cloud Water Path ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Satellite observations are used to deduce the relationship between cloud water and precipitation water for low-latitude shallow marine clouds. The specific sensors that facilitate the analysis are the collocated CloudSat profiling radar and the Moderate Resolution Imaging Spectroradiometer (MODIS). The separation of the cloud water and precipitation water signals relies on the relative insensitivity of MODIS to the presence of precipitation water in conjunction with estimates of the path-integrated attenuation of the CloudSat radar beam while explicitly accounting for the effect of precipitation water on the observed MODIS optical depth. Variations in the precipitation water path are shown to be associated with both the cloud water path and the cloud effective radius, suggesting both macrophysical and microphysical controls on the production of precipitation water. The method outlined here is used to place broad bounds on the mean relationship between the precipitation water path and the cloud water path in shallow marine clouds, given certain clearly stated assumptions. The ratio of precipitation water to cloud water is shown to increase from zero at low cloud water path values to roughly 0.5 at 500 g m−2 of cloud water. The retrieval results further show that the median influence of precipitation on the observed optical depth increases monotonically with optical depth varying between 1% and 5% at 500 g m−2 of cloud water with the source of the uncertainty deriving from the assumption of the nature of the precipitation drop size distribution.

scholarly journals A Study of the Distribution and Variability of Cloud Water Using ISCCP, SSM/I Cloud Product, and Reanalysis Datasets

2014 ◽  
Vol 27 (9) ◽  
pp. 3114-3128 ◽  
Author(s):  
Zhiwei Heng ◽  
Yunfei Fu ◽  
Guosheng Liu ◽  
Renjun Zhou ◽  
Yu Wang ◽  
...  
Keyword(s):  
Cloud Water ◽  
Positive Anomaly ◽  
Liquid Water Path ◽  
Water Path ◽  
The North ◽  
Increasing Trend ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Microwave Imager ◽  
Ice Water

Abstract In this paper, the global distribution of cloud water based on International Satellite Cloud Climatology Project (ISCCP), Moderate Resolution Imaging Spectroradiometer (MODIS), CloudSat Cloud Profiling Radar (CPR), European Center for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim), and Climate Forecast System Reanalysis (CFSR) datasets is presented, and the variability of cloud water from ISCCP, the Special Sensor Microwave Imager (SSM/I), ERA-Interim, and CFSR data over the time period of 1995 through 2009 is discussed. The results show noticeable differences in cloud water over land and over ocean, as well as latitudinal variations. Large values of cloud water are mainly distributed over the North Pacific and Atlantic Oceans, eastern ITCZ, regions off the west coast of the continents as well as tropical rain forest. Cloud water path (CWP), liquid water path (LWP), and ice water path (IWP) from these datasets show a relatively good agreement in distributions and zonal means. The results of trend analyzing show an increasing trend in CWP, and also a significant increasing trend of LWP can be found in the dataset of ISCCP, ERA-Interim, and CFSR over the ocean. Besides the long-term variation trend, rises of cloud water are found when temperature and water vapor exhibit a positive anomaly. EOF analyses are also applied to the anomalies of cloud water, the first dominate mode of CWP and IWP are similar, and a phase change can be found in the LWP time coefficient around 1999 in ISCCP and CFSR and around 2002 in ERA-Interim.

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

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 The Response of Cloud-Precipitation Recycling in China to Global Warming

2021 ◽  
Vol 13 (8) ◽  
pp. 1601
Author(s):  
Qi Guo ◽  
Xianjie Cao ◽  
Jiening Liang ◽  
Zhida Zhang ◽  
Min Zhang ◽  
...  
Keyword(s):  
Moisture Flux ◽  
Northwest China ◽  
Cloud Water ◽  
Feedback Mechanism ◽  
Climate Feedback ◽  
Water Path ◽  
Precipitation Efficiency ◽  
Meteorological Observatory ◽  
Cloud Water Path ◽  
Moderate Resolution Imaging Spectroradiometer

Cloud water is an important geophysical quantity that connects the hydrological and radiation characteristics of climate systems and plays an essential role in the global circulation of the atmosphere, water, and energy. However, compared to the contribution of water vapor to precipitation, the understanding of cloud-precipitation transformation and its climate feedback mechanism remains limited. Based on precipitation and temperature datasets of the National Meteorological Observatory and MODIS (Moderate Resolution Imaging Spectroradiometer) satellite remote sensing products, the evolution characteristics of cloud water resources in China over the last twenty years of the 21st century were evaluated. Significant decreasing trends of −3.3 and −4.89 g/m2 decade-1 were found for both the liquid and ice water path. In humid areas with high precipitation, the cloud water path decreased fast. In semiarid areas with an annual precipitation ranging from 500–800 mm, the decreasing trend of the cloud water path was the lowest. The cloud-water period was calculated to represent the relative changes in clouds and precipitation. The national average cloud-water period in China is approximately 12.4 h, with obvious seasonal changes. Over the last 20 years, the cloud water path in dry regions decreased more slowly than that in wet regions, and the cloud-precipitation efficiency significantly increased, which narrowed the climate difference between the dry and wet regions. Finally, the mechanism of the cloud-water period evolution in the different regions were examined from the perspectives of the dynamic and thermal contributions, respectively. Due to the overall low upward moisture flux (UMF) in the dry region, the response of the cloud-water period to the lower tropospheric stability (LTS) mainly first increased and then decreased, which was the opposite in the wet region. The increase in cloud-precipitation efficiency in the dry region of Northwest China is accompanied by a continuous decrease in LTS. The different configurations of regional UMF and LTS play a crucial role in the evolution of cloud-precipitation, which can be used as a diagnostic basis to predict changes in the precipitation intensity to a certain extent.

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 Evaluation of WRF-DART (ARW v3.9.1.1 and DART Manhattan release) multiphase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

2019 ◽  
Vol 12 (9) ◽  
pp. 3939-3954
Author(s):  
Frederik Kurzrock ◽  
Hannah Nguyen ◽  
Jerome Sauer ◽  
Fabrice Chane Ming ◽  
Sylvain Cros ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Cloud Water ◽  
Specific Humidity ◽  
Reunion Island ◽  
Short Term ◽  
Water Path ◽  
Réunion Island ◽  
Cloud Water Path ◽  
The Impact

Abstract. Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in midlatitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days are still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure and on the impact of this method on short-term forecasts of GHI for Réunion Island, a tropical island in the southwest Indian Ocean. The multilayer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) Manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated, and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and middle troposphere. Ground-based GHI observations at 12 sites on Réunion Island are used to quantify the impact of CWP DA. Over a total of 44 d during austral summertime, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 h. Root mean square error and mean absolute error are reduced by 4 % and 3 %, respectively.

scholarly journals Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance

2015 ◽  
Vol 8 (10) ◽  
pp. 4083-4110 ◽  
Author(s):  
R. C. Levy ◽  
L. A. Munchak ◽  
S. Mattoo ◽  
F. Patadia ◽  
L. A. Remer ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Infrared Imaging ◽  
Quantitative Information ◽  
Environmental Data ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
Data Record ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 μm AOD and 0.55/0.86 μm-based Ångström Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March–April–May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ~ 0.025), while reducing the differences between AE. We characterize algorithm retrievability through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.

scholarly journals Joint retrievals of cloud and drizzle in marine boundary layer clouds using ground-based radar, lidar and zenith radiances

2015 ◽  
Vol 8 (7) ◽  
pp. 2663-2683 ◽  
Author(s):  
M. D. Fielding ◽  
J. C. Chiu ◽  
R. J. Hogan ◽  
G. Feingold ◽  
E. Eloranta ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Marine Boundary Layer ◽  
Microwave Radiometer ◽  
Cloud Water ◽  
Radar Reflectivity ◽  
New Method ◽  
Error Statistics ◽  
Water Path ◽  
Boundary Layer Clouds ◽  
Cloud Water Path

Abstract. Active remote sensing of marine boundary-layer clouds is challenging as drizzle drops often dominate the observed radar reflectivity. We present a new method to simultaneously retrieve cloud and drizzle vertical profiles in drizzling boundary-layer clouds using surface-based observations of radar reflectivity, lidar attenuated backscatter, and zenith radiances under conditions when precipitation does not reach the surface. Specifically, the vertical structure of droplet size and water content of both cloud and drizzle is characterised throughout the cloud. An ensemble optimal estimation approach provides full error statistics given the uncertainty in the observations. To evaluate the new method, we first perform retrievals using synthetic measurements from large-eddy simulation snapshots of cumulus under stratocumulus, where cloud water path is retrieved with an error of 31 g m−2. The method also performs well in non-drizzling clouds where no assumption of the cloud profile is required. We then apply the method to observations of marine stratocumulus obtained during the Atmospheric Radiation Measurement MAGIC deployment in the Northeast Pacific. Here, retrieved cloud water path agrees well with independent three-channel microwave radiometer retrievals, with a root mean square difference of 10–20 g m−2.

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