Dataset Information

An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path.

ABSTRACT: The first extended comprehensive data set of the retrieval uncertainties in passive microwave observations of cloud liquid water path (CLWP) for warm oceanic clouds has been created for practical use in climate applications. Four major sources of systematic errors were considered over the 9-year record of the Advanced Microwave Scanning Radiometer-EOS (AMSR-E): clear-sky bias, cloud-rain partition (CRP) bias, cloud-fraction-dependent bias, and cloud temperature bias. Errors were estimated using a unique merged AMSR-E/Moderate resolution Imaging Spectroradiometer Level 2 data set as well as observations from the Cloud-Aerosol Lidar with Orthogonal Polarization and the CloudSat Cloud Profiling Radar. To quantify the CRP bias more accurately, a new parameterization was developed to improve the inference of CLWP in warm rain. The cloud-fraction-dependent bias was found to be a combination of the CRP bias, an in-cloud bias, and an adjacent precipitation bias. Globally, the mean net bias was 0.012 kg/m2, dominated by the CRP and in-cloud biases, but with considerable regional and seasonal variation. Good qualitative agreement between a bias-corrected AMSR-E CLWP climatology and ship observations in the Northeast Pacific suggests that the bias estimates are reasonable. However, a possible underestimation of the net bias in certain conditions may be due in part to the crude method used in classifying precipitation, underscoring the need for an independent method of detecting rain in warm clouds. This study demonstrates the importance of combining visible-infrared imager data and passive microwave CLWP observations for estimating uncertainties and improving the accuracy of these observations.

SUBMITTER: Greenwald TJ

PROVIDER: S-EPMC5993219 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path.

Greenwald Thomas J TJ Bennartz Ralf R Lebsock Matthew M Teixeira João J

Journal of geophysical research. Atmospheres : JGR 20180412 7

The first extended comprehensive data set of the retrieval uncertainties in passive microwave observations of cloud liquid water path (CLWP) for warm oceanic clouds has been created for practical use in climate applications. Four major sources of systematic errors were considered over the 9-year record of the Advanced Microwave Scanning Radiometer-EOS (AMSR-E): clear-sky bias, cloud-rain partition (CRP) bias, cloud-fraction-dependent bias, and cloud temperature bias. Errors were estimated using ...[more]

PMID: 29938146

Similar Datasets

Project description:We investigated the view angle dependence of domain mean Moderate Resolution Imaging Spectroradiometer (MODIS) liquid water path (LWP) and that of corresponding cloud optical thickness, effective radius, and liquid cloud fraction as proxy for plane-parallel retrieval biases. Independent Advanced Microwave Scanning Radiometer-EOS LWP was used to corroborate that the observed variations with sun-view geometry were not severely affected by seasonal/latitudinal changes in cloud properties. Microwave retrievals showed generally small (<10%) cross-swath variations. The view angle (cross-swath) dependence of MODIS optical thickness was weaker in backscatter than forward scatter directions and transitioned from mild ∩ shape to stronger ∪ shape as heterogeneity, sun angle, or latitude increased. The 2.2 µm effective radius variations always had a ∪ shape, which became pronounced and asymmetric toward forward scatter in the most heterogeneous clouds and/or at the lowest sun. Cloud fraction had the strongest and always ∪-shaped view angle dependence. As a result, in-cloud MODIS cloud liquid water path (CLWP) showed surprisingly good view angle (cross-swath) consistency, usually comparable to that of microwave retrievals, due to cancelation between optical thickness and effective radius biases. Larger (20-40%) nadir-relative increases were observed in the most extreme heterogeneity and sun angle bins, that is, typically in the polar regions, which, however, constituted only 3-8% of retrievals. The good consistency of MODIS in-cloud CLWP was lost for gridbox mean LWP, which was dominated by the strong cloud fraction increase with view angle. More worryingly, MODIS LWP exhibited significant and systematic absolute increases with heterogeneity and sun angle that is not present in microwave LWP.Key pointsMicrowave LWP shows small overall and cross-swath variationsMODIS in-cloud LWP also shows good view angle consistency in most casesMODIS retrievals show strong overall increase with heterogeneity and sun angle.

Project description:Land surface evapotranspiration (ET) is one of the main energy sources for atmospheric dynamics and a critical component of the local, regional, and global water cycles. Consequently, accurate measurement or estimation of ET is one of the most active topics in hydro-climatology research. With massive and spatially distributed observational data sets of land surface properties and environmental conditions being collected from the ground, airborne or space-borne platforms daily over the past few decades, many research teams have started to use big data science to advance the ET estimation methods. The Geostationary satellite Evapotranspiration and Drought (GET-D) product system was developed at the National Oceanic and Atmospheric Administration (NOAA) in 2016 to generate daily ET and drought maps operationally. The primary inputs of the current GET-D system are the thermal infrared (TIR) observations from NOAA GOES satellite series. Because of the cloud contamination to the TIR observations, the spatial coverage of the daily GET-D ET product has been severely impacted. Based on the most recent advances, we have tested a machine learning algorithm to estimate all-weather land surface temperature (LST) from TIR and microwave (MW) combined satellite observations. With the regression tree machine learning approach, we can combine the high accuracy and high spatial resolution of GOES TIR data with the better spatial coverage of passive microwave observations and LST simulations from a land surface model (LSM). The regression tree model combines the three LST data sources for both clear and cloudy days, which enables the GET-D system to derive an all-weather ET product. This paper reports how the all-weather LST and ET are generated in the upgraded GET-D system and provides an evaluation of these LST and ET estimates with ground measurements. The results demonstrate that the regression tree machine learning method is feasible and effective for generating daily ET under all weather conditions with satisfactory accuracy from the big volume of satellite observations.

Dataset Information

An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path.

Publications

An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets