Assessing observed and modelled spatial distributions of ice water path  using satellite data

Eliasson, Salomon; Buehler, Stefan A.; Milz, M.; Eriksson, Patrick; John, Viju O.

doi:10.5194/acp-11-375-2011

Cited by 102 publications

(129 citation statements)

References 27 publications

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“…The errors in liquid drop size, surface emission/scattering, cloud layer height, and water vapor amount can all degrade the quality of the retrieved IWP. For example, the current operational IWP retrieval algorithm from Microwave Surface and Precipitation Products System (MSPPS), which is based upon a two-stream approximated radiative model solution (Zhao and Weng, 2002) at AMSU-B 89 and 150 GHz channels, was found to under-estimate IWP in comparison with other observations Waliser et al, 2009;Eliasson et al, 2011;Chen et al, 2011). Contamination of cloud ice retrievals was also found over snowy/icy surfaces .…”

Section: Introductionmentioning

confidence: 94%

“…However, observations of cloud ice have not met the requirement by climate models, showing several folds of IWP differences among various techniques Eliasson et al, 2011). Until cross-instrument consistency is achieved, current cloud ice observations will allow too much variation in cloud properties and become insufficient for constraining the model physics Li et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Like other A-train sun-synchronous satellites, it samples only two local solar times (01:30 and 13:30 LST) of the cloud diurnal cycle. However, CloudSat data still provide the best characterization of the vertical distribution of global cloud ice (Eliasson et al, 2011), and can be used to cross-calibrate other techniques, especially the passive sensors with limited vertical resolution. .…”

Section: Introductionmentioning

confidence: 99%

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CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances

Gong

2014

Atmos. Meas. Tech.

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Abstract. Ice water path (IWP) and cloud top height (h t ) are two of the key variables in determining cloud radiative and thermodynamical properties in climate models. Large uncertainty remains among IWP measurements from satellite sensors, in large part due to the assumptions made for cloud microphysics in these retrievals. In this study, we develop a fast algorithm to retrieve IWP from the 157, 183.3 ± 3 and 190.3 GHz radiances of the Microwave Humidity Sounder (MHS) such that the MHS cloud ice retrieval is consistent with CloudSat IWP measurements. This retrieval is obtained by constraining the empirical forward models between collocated and coincident measurements of CloudSat IWP and MHS cloud-induced radiance depression (T cir ) at these channels. The empirical forward model is represented by a lookup table (LUT) of T cir -IWP relationships as a function of h t and the frequency channel. With h t simultaneously retrieved, the IWP is found to be more accurate. The useful range of the MHS IWP retrieval is between 0.5 and 10 kg m −2 , and agrees well with CloudSat in terms of the normalized probability density function (PDF). Compared to the empirical model, current operational radiative transfer models (RTMs) still have significant uncertainties in characterizing the observed T cir -IWP relationships. Therefore, the empirical LUT method developed here remains an effective approach to retrieving ice cloud properties from the MHS-like microwave channels.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances

Gong

2014

Atmos. Meas. Tech.

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“…Global average ice water path varies by more than order of magnitude (0.01 and 0.2 kg m −2 ) between different climate models (Solomon et al, 2007). Unfortunately, a similar spread also exists amongst satellite estimates (Eliasson et al, 2011), which makes it difficult to make progress. Measuring snowfall, an important component of the water cycle (Mugnai et al, 2005), is an even more complicated matter because of the enormous complexity of snow crystal habit, density, and particle size distribution.…”

Section: Gaps In Cloud Profiling Observational Capabilitiesmentioning

confidence: 99%

G band atmospheric radars: new frontiers in cloud physics

et al. 2014

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Abstract. Clouds and associated precipitation are the largest source of uncertainty in current weather and future climate simulations. Observations of the microphysical, dynamical and radiative processes that act at cloud scales are needed to improve our understanding of clouds. The rapid expansion of ground-based super-sites and the availability of continuous profiling and scanning multi-frequency radar observations at 35 and 94 GHz have significantly improved our ability to probe the internal structure of clouds in high temporal-spatial resolution, and to retrieve quantitative cloud and precipitation properties. However, there are still gaps in our ability to probe clouds due to large uncertainties in the retrievals.The present work discusses the potential of G band (frequency between 110 and 300 GHz) Doppler radars in combination with lower frequencies to further improve the retrievals of microphysical properties. Our results show that, thanks to a larger dynamic range in dual-wavelength reflectivity, dual-wavelength attenuation and dual-wavelength Doppler velocity (with respect to a Rayleigh reference), the inclusion of frequencies in the G band can significantly improve current profiling capabilities in three key areas: boundary layer clouds, cirrus and mid-level ice clouds, and precipitating snow.

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“…This requires satellite retrievals to make unique assumptions to estimate the geophysical parameters from space. While retrieved geophysical parameters are useful to quantify geophysical parameters (often called Level 2 and Level 3 data sets), it often frustrates evaluating atmospheric models in detail due to different physical assumptions between various satellite retrievals and Earth System models [Eliasson et al, 2011;Waliser et al, 2009].…”

Section: Introductionmentioning

confidence: 99%

Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling

Matsui

Santanello

Shi

et al. 2014

J. Geophys. Res. Atmos.

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Earth System modeling has become more complex, and its evaluation using satellite data has also become more difficult due to model and data diversity. Therefore, the fundamental methodology of using satellite direct measurements with instrumental simulators should be addressed especially for modeling community members lacking a solid background of radiative transfer and scattering theory. This manuscript introduces principles of multisatellite, multisensor radiance-based evaluation methods for a fully coupled regional Earth System model: NASA-Unified Weather Research and Forecasting (NU-WRF) model. We use a NU-WRF case study simulation over West Africa as an example of evaluating aerosol-cloud-precipitation-land processes with various satellite observations. NU-WRF-simulated geophysical parameters are converted to the satellite-observable raw radiance and backscatter under nearly consistent physics assumptions via the multisensor satellite simulator, the Goddard Satellite Data Simulator Unit. We present varied examples of simple yet robust methods that characterize forecast errors and model physics biases through the spatial and statistical interpretation of various satellite raw signals: infrared brightness temperature (Tb) for surface skin temperature and cloud top temperature, microwave Tb for precipitation ice and surface flooding, and radar and lidar backscatter for aerosol-cloud profiling simultaneously. Because raw satellite signals integrate many sources of geophysical information, we demonstrate user-defined thresholds and a simple statistical process to facilitate evaluations, including the infrared-microwave-based cloud types and lidar/radar-based profile classifications.

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Assessing observed and modelled spatial distributions of ice water path using satellite data

Cited by 102 publications

References 27 publications

CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances

CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances

G band atmospheric radars: new frontiers in cloud physics

Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling

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