SPARE‐ICE: Synergistic ice water path from passive operational sensors

Holl, Gerrit; Eliasson, Salomon; Mendrok, Jana; Buehler, Stefan A.

doi:10.1002/2013jd020759

Cited by 46 publications

(58 citation statements)

References 57 publications

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“…One should always keep in mind that this is the error on top of the CloudSat IWP retrieval uncertainties. SPARE-ICE by Holl et al (2014) showed a larger error in general, but their algorithm could quantitatively evaluate the contribution from each source, including CloudSat itself. The two algorithms are not directly comparable since we are under different metrics.…”

Section: Error Analysismentioning

confidence: 99%

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: Error Analysismentioning

confidence: 99%

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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“…If the error from imperfect collocations is random, this will have a limited effect on the ANN. Only if there is a recurrent systematic difference as a result of the different spatial scales this will lead to biased retrievals (Holl et al, 2014). (2) Although cirrus clouds leave their mark on both SEVIRI and CALIOP measurements in a similar way, SEVIRI does not share CALIOP's possibility of discerning vertically separated ice clouds, liquid water clouds and aerosols.…”

Section: Data Collocationmentioning

confidence: 99%

“…Minnis et al (2016) present a similar approach to estimate the optical thickness of opaque ice clouds at night using an ANN trained with coincident CloudSat/CPR (Cloud Profiling Radar) measurements and Aqua/MODIS (Moderate Resolution Imaging Spectroradiometer) infrared radiances. Holl et al (2014) use combined CALIPSO/CALIOP and CloudSat/CPR retrievals for the retrieval of the IWP from AVHRR (Advanced Very High Resolution Radiometer) and MHS (Microwave Humidity Sounder) on the NOAA and Metop satellites using neural networks. Cerdena et al (2006Cerdena et al ( , 2007 use neural networks trained with simulated radiances for the retrieval of optical thickness, effective radius and temperature of liquid water clouds (day and night) and cirrus clouds (only day) from NOAA/AVHRR.…”

Section: Introductionmentioning

confidence: 99%

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

et al. 2017

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Abstract. Cirrus clouds play an important role in climate as they tend to warm the Earth-atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SE-VIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data.CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m −2 . Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness.By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.

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“…King et al, 1998;Sourdeval et al, 2013). Direct retrievals of the vertically integrated ice water content (IWC) -the ice water path (IWP) -can also be obtained from these channels (Sourdeval et al, 2015), passive microwave sensors (Gong and Wu, 2014), or a synergy of both (Holl et al, 2014). Vertical profiles of the cloud visible extinction (α ext ), IWC and ice crystal r eff are 10 commonly provided using lidar and/or radar measurements (e.g.…”

Section: Introductionmentioning

confidence: 99%

Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation

Sourdeval¹,

Gryspeerdt²,

Krämer³

et al. 2018

Preprint

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Abstract. The number concentration of cloud particles is a key quantity for understanding aerosol-cloud interactions and describing clouds in climate and numerical weather prediction models. In contrast with recent advances for liquid clouds, few observational constraints exist on the ice crystal number concentration (N i ). This study investigates how combined lidar-radar measurements can be used to provide satellite estimates of N i , using a methodology that constrains moments of a parameterized particle size distribution (PSD). The operational liDAR-raDAR (DARDAR) product serves as an existing base for this method, 5 which focuses on ice clouds with temperatures T c < -30°C.Theoretical considerations demonstrate the capability for accurate retrievals of N i , apart from a possible bias in the concentration in small crystals when T c -50°C, due to the assumption of a monomodal PSD shape in the current method. This is verified by comparing satellite estimates to co-incident in situ measurements, which additionally demonstrates the sufficient sensitivity of lidar-radar observations to N i . Following these results, satellite estimates of N i are evaluated in the context of a 10 case study and a preliminary climatological analysis based on 10 years of global data. Despite of a lack of other large-scale references, this evaluation shows a reasonable physical consistency in N i spatial distribution patterns. Notably, increases in N i are found towards cold temperatures and, more significantly, in the presence of strong updraughts, such as those related to convective or orographic uplifts. Further evaluation and improvements of this method are necessary but these results already constitute a first encouraging step towards large-scale observational constraints for N i . Part two of this series uses this new 15 dataset to examine the controls on N i .

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SPARE‐ICE: Synergistic ice water path from passive operational sensors

Cited by 46 publications

References 57 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

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation

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