Evaluating specific error characteristics of microwave‐derived cloud liquid water products

Greenwald, Thomas J.; L’Ecuyer, Tristan; Christopher, Sundar A.

doi:10.1029/2007gl031180

Cited by 33 publications

(53 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the southern midlatitudes, the positive differences could result from overestimates of τ and/or r e by CERES due to large SZAs or underestimates by AMSR-E due to high wind speeds and low PW values [43]. As noted in Section IV-A, the ozone optical-depth underestimation in the VISST retrievals causes an overestimation of τ .…”

Section: B Cloud Microphysical Properties 1) Liquid Cloudsmentioning

confidence: 95%

“…In the tropics, the mean values of τ and/or r e from CERES could be too low or the LWP from AMSR-E could be too large. A sensitivity study [43] of the AMSR-E retrievals reveals that, for areas with total precipitable water (PW) exceeding 6 cm, the LWP can be overestimated by 50 g · m −2 or more. The maximum difference for the tropics in Fig.…”

Section: B Cloud Microphysical Properties 1) Liquid Cloudsmentioning

confidence: 99%

See 1 more Smart Citation

CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part II: Examples of Average Results and Comparisons With Other Data

Minnis

Sun-Mack

Chen

et al. 2011

IEEE Trans. Geosci. Remote Sensing

131

135

View full text Add to dashboard Cite

Cloud properties were retrieved by applying the Clouds and Earth's Radiant Energy System (CERES) project Edition-2 algorithms to 3.5 years of Tropical Rainfall Measuring Mission Visible and Infrared Scanner data and 5.5 and 8 years of MODerate Resolution Imaging Spectroradiometer (MODIS) data from Aqua and Terra, respectively. The cloud products are consistent quantitatively from all three imagers; the greatest discrepancies occur over ice-covered surfaces. The retrieved cloud cover (∼59%) is divided equally between liquid and ice clouds. Global mean cloud effective heights, optical depth, effective particle sizes, and water paths are 2.5 km, 9.9, 12.9 μm, and 80 g · m −2 , respectively, for liquid clouds and 8.3 km, 12.7, 52.2 μm, and 230 g · m −2 for ice clouds. Cloud droplet effective radius is greater over ocean than land and has a pronounced seasonal cycle over southern oceans. Comparisons with independent measurements from surface sites, the Ice Cloud and Land Elevation Satellite, and the Aqua Advanced Microwave Scanning Radiometer-Earth Observing System are used to evaluate the results. The mean CERES and MODIS Atmosphere Science Team cloud properties have many similarities but exhibit large discrepancies in certain parameters due to differences in the algorithms and the number of unretrieved cloud pixels. Problem areas in the CERES algorithms are identified and discussed.Index Terms-Climate, cloud, cloud remote sensing, Clouds and the Earth's Radiant Energy System (CERES), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible and Infrared Scanner (VIRS).

show abstract

Section: B Cloud Microphysical Properties 1) Liquid Cloudsmentioning

confidence: 95%

Section: B Cloud Microphysical Properties 1) Liquid Cloudsmentioning

confidence: 99%

CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part II: Examples of Average Results and Comparisons With Other Data

Minnis

Sun-Mack

Chen

et al. 2011

IEEE Trans. Geosci. Remote Sensing

131

135

View full text Add to dashboard Cite

show abstract

“…If only cloud‐free observations are sampled individual LWP observations should ideally not retrieve an exact value of zero but a Gaussian distribution of LWP around zero with a half‐width corresponding to the expected uncertainty in LWP retrievals. This uncertainty lies somewhere between 15 gm −2 and 30 gm −2 [ Bennartz , 2007; Greenwald et al , 2007; Horváth and Gentemann , 2007].…”

Section: Determination Of All‐sky and Clear‐sky Water Vapor Path (Wvp)mentioning

confidence: 99%

Contribution of water vapor to observational estimates of longwave cloud radiative forcing

Sohn

Bennartz

2008

J. Geophys. Res.

View full text Add to dashboard Cite

[1] It has been long recognized that cloud-free regions are typically associated with drier air, whereas cloudy regions typically are associated with more moisture. Using Advanced Microwave Scanning Radiometer (AMSR-E) passive microwave observations, we estimated the bias between water vapor path (WVP) of clear-sky and all-sky conditions. Furthermore, we assessed the impact of these biases on longwave cloud radiative forcing (CRF). Results indicate that, in a zonal mean sense, the difference between all-sky and clear-sky WVP estimates is around 2 mm, with highest values in the midlatitudes summer, where differences up to 7 mm occur. The corresponding CRF change forced by these WVP changes is about 2 W m À2 in a zonal mean sense. Highest values occur in the midlatitudes of the northern hemisphere in which a magnitude up to 6 W m À2 is shown. Over the convectively active regions, the WVP contributed CRF appears to be smaller than 5 W m À2 , substantially smaller than the CRF contributed by upper tropospheric water vapor only, suggesting that a significant portion of satellite-estimated CRF over the tropics is contributed by the redistribution of water vapor associated with convection development.Citation: Sohn, B.-J., and R. Bennartz (2008), Contribution of water vapor to observational estimates of longwave cloud radiative forcing,

show abstract

“…AMSR-E has a coarser spatial resolution than MODIS. Its LWP retrievals are available for both daytime and nighttime but suffer from instrument noise, cloud detection issues, and beam filling effect (Greenwald et al, 2007;Horváth and Gentemann, 2007;Seethala and Horváth, 2010). MODIS LWP retrievals are available only during daytime.…”

Section: Introductionmentioning

confidence: 99%

Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites

et al. 2018

View full text Add to dashboard Cite

Abstract. Precipitation susceptibility to aerosol perturbation plays a key role in understanding aerosol-cloud interactions and constraining aerosol indirect effects. However, large discrepancies exist in the previous satellite estimates of precipitation susceptibility. In this paper, multi-sensor aerosol and cloud products, including those from the CloudAerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) from June 2006 to April 2011 are analyzed to estimate precipitation frequency susceptibility S POP , precipitation intensity susceptibility S I , and precipitation rate susceptibility S R in warm marine clouds. We find that S POP strongly depends on atmospheric stability, with larger values under more stable environments. Our results show that precipitation susceptibility for drizzle (with a −15 dBZ rainfall threshold) is significantly different than that for rain (with a 0 dBZ rainfall threshold). Onset of drizzle is not as readily suppressed in warm clouds as rainfall while precipitation intensity susceptibility is generally smaller for rain than for drizzle. We find that S POP derived with respect to aerosol index (AI) is about one-third of S POP derived with respect to cloud droplet number concentration (CDNC). Overall, S POP demonstrates relatively robust features throughout independent liquid water path (LWP) products and diverse rain products. In contrast, the behaviors of S I and S R are subject to LWP or rain products used to derive them. Recommendations are further made for how to better use these metrics to quantify aerosolcloud-precipitation interactions in observations and models.

show abstract

Evaluating specific error characteristics of microwave‐derived cloud liquid water products

Cited by 33 publications

References 25 publications

CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part II: Examples of Average Results and Comparisons With Other Data

CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part II: Examples of Average Results and Comparisons With Other Data

Contribution of water vapor to observational estimates of longwave cloud radiative forcing

Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites

Contact Info

Product

Resources

About