Search

Ice clouds are a crucial component of the Earth's weather system, and their representation remains a principal challenge for current weather and climate models. Several past and future satellite missions were explicitly designed to provide observations offering new insights into cloud processes, but these specialized cloud sensors are limited in their spatial and temporal coverage. Geostationary satellites have been observing clouds for several decades and can ideally complement the sparse measurements from specialized cloud sensors. However, the geostationary observations that are continuously and globally available over the full observation record are restricted to a small number of wavelengths, which limits the information they can provide on clouds. The Chalmers Cloud Ice Climatology (CCIC) is a novel cloud-property dataset that aims to provide an improved climate record of ice hydrometeor concentrations by applying state-of-the-art machine-learning techniques to retrieve ice cloud properties from globally gridded, single-channel geostationary observations that are readily available from 1980 onwards. CCIC offers a novel perspective on the record of geostationary IR observations by providing spatially and temporally continuous retrievals of the vertically integrated and vertically resolved concentrations of frozen hydrometeors, typically referred to as ice water path (IWP) and ice water content (IWC). In addition to that, CCIC provides 2D and 3D cloud masks and a 3D cloud classification. A fully convolutional quantile regression neural network constitutes the core of the CCIC retrieval, providing probabilistic estimates of IWP and IWC. The network is trained against CloudSat retrievals using 3.5 years of global collocations. Assessed on a held-out test dataset, the CCIC-provided IWP and IWC estimates achieve correlations exceeding 0.7 and 0.6, respectively, and biases better than - 5 % and - 2 % demonstrating considerable skill in estimating both IWP and IWC. In addition, CCIC is extensively validated against both in situ and remote sensing measurements from two flight campaign series and a ground-based radar. The results of this independent validation confirm the ability of CCIC to retrieve IWP and IWC. CCIC thus ideally complements temporally and spatially more limited measurements from dedicated cloud sensors by providing spatially and temporally continuous estimates of ice cloud properties. The CCIC network and its associated software are made accessible to the scientific community. [ABSTRACT FROM AUTHOR]

The first closure study involving passive microwave and submillimetre measurements of ice clouds with the consideration of oriented particles is presented, using a unique combination of polarised observations from the ISMAR spectral-like radiometer, two radars with frequencies of 35 and 95 GHz , and a variety of in situ instruments. Of particular interest to this study are the large V–H polarised brightness temperature differences measured from ISMAR above a thick frontal ice cloud. Previous studies combining radar and passive submillimetre measurements have not considered polarisation differences. Moreover, they have assumed particle habits a priori. We aim to test whether the large V–H measurements can be simulated successfully by using an atmospheric model consistent with in situ microphysics. An atmospheric model is constructed using information from the in situ measurements, such as the ice water content, the particle size distribution, and the mass and shape of particles, as well as background information obtained from dropsonde profiles. Columnar and dendritic aggregate particle models are generated specifically for this case, and their scattering properties are calculated using the independent monomer approximation under the assumption of horizontal orientation. The scattering properties are used to perform polarised radiative transfer simulations using ARTS to test whether we can successfully simulate the measured large V–H differences. Radar measurements are used to extrapolate the 1-D microphysical profile to derive a time series of particle size distributions which are used to simulate ISMAR brightness temperatures. These simulations are compared to the observations. It is found that particle models that are consistent with in situ microphysics observations are capable of reproducing the brightness temperature depression and polarisation signature measured from ISMAR at the dual-polarised channel of 243 GHz. However, it was required that a proportion of the particles were changed in order to increase the V–H polarised brightness temperature differences. Thus, we incorporated millimetre-sized dendritic crystals, as these particles were observed in the probe imagery. At the second dual-polarised channel of 664 GHz , the brightness temperature depressions were generally simulated at the correct locations; however, the simulated V–H was too large. This work shows that multi-frequency polarisation information could be used to infer realistic particle shapes, orientations, and representations of the split between single crystals and aggregates within the cloud. [ABSTRACT FROM AUTHOR]

The Ice Cloud Imager (ICI) aboard the Second Generation of the EUMETSAT Polar System (EPS-SG) will provide novel measurements of ice hydrometeors. ICI is a passive conically scanning radiometer that will operate within a frequency range of 183 GHz to 664 GHz, helping to cover the present wavelength gap between microwave and infrared observations. Reliable global data will be produced on a daily basis. This paper presents the retrieval database to be used operationally and performs a final pre-launch assessment of ICI retrievals. Simulations are performed within atmospheric states that are consistent with radar reflectivities and represent the three-dimensional variability of clouds. The radiative transfer calculations use empirically-based hydrometeor models. Azimuthal orientation of particles is mimicked, allowing for polarisation to be considered. The degrees of freedom of the ICI retrieval database are shown to vary according to cloud type. The simulations are considered to be the most detailed performed to this date. Simulated radiances are shown to be statistically consistent with real observations. Machine learning is applied to perform inversions of the simulated ICI observations. The method used allows for the estimation of non-Gaussian uncertainties for each retrieved case. Retrievals of ice water path (IWP), mean mass height (Zm) and mean mass diameter (Dm) are presented. Distributions and zonal means of both database and retrieved IWP show agreement with DARDAR. Retrieval tests indicate that ICI will be sensitive to IWP between 10-2 and 101 kg m-2. Retrieval performance is shown to vary with climatic region and surface type, with the best performance achieved over tropical regions and over ocean. As a consequence of this study, retrievals on real observations will be possible from day one of the ICI operational phase. [ABSTRACT FROM AUTHOR]

The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates retrievals of liquid precipitation over snow-free and non-mountainous surfaces from GPROF V7 and multiple configurations of GPROF-NN against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years, and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable estimates of liquid precipitation over the CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reliably reproduces each region's diurnal and seasonal precipitation characteristics. The effective resolution of GPROF V7 is found to be 51 km over the CONUS and 18 km over the tropical Pacific. GPROF V7 also produces robust precipitation estimates for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPM PMW precipitation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error of instantaneous precipitation estimates by about 20 % for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar–radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used to generate the training data for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals. [ABSTRACT FROM AUTHOR]

Ice clouds are a crucial component of the Earth's weather system, and their representation remains a principal challenge for current weather and climate models. Several past and future satellite missions were explicitly designed to provide observations offering new insights into cloud processes, but these specialized cloud sensors are limited in their spatial and temporal coverage. Geostationary satellites have been observing clouds for several decades and can ideally complement the sparse measurements from specialized cloud sensors. However, the geostationary observations that are continuously and globally available over the full observation record are restricted to a small number of wavelengths, which limits the information they can provide on clouds. The Chalmers Cloud Ice Climatology (CCIC) addresses this challenge by applying novel machine-learning techniques to retrieve ice cloud properties from globally gridded, single-channel geostationary observations that are readily available from 1980 onwards. CCIC aims to offer a novel perspective on the record of geostationary IR observations by providing spatially and temporally continuous retrievals of the vertically-integrated and vertically-resolved concentrations of frozen hydrometeors, typically referred to as ice water path (IWP) and ice water content (IWC). In addition to that, CCIC provides 2D and 3D cloud masks and a 3D cloud classification. A fully convolutional quantile regression neural network constitutes the core of the CCIC retrieval, providing probabilistic estimates of IWP and IWC. The network is trained against CloudSat retrievals using 3.5 years of global collocations. Assessment of the retrieval accuracy on a held-out test set demonstrates considerable skill in reproducing the reference IWP and IWC estimates. In addition, CCIC is extensively validated against both in-situ and remote sensing measurements from two flight campaigns and a ground-based radar. The results of this independent validation confirm the ability of CCIC to retrieve IWP and, to first order, even IWC. CCIC thus ideally complements temporally and spatially more limited measurements from dedicated cloud sensors by providing spatially and temporally continuous estimates of ice cloud properties. The CCIC network and its associated software are made accessible to the scientific community. [ABSTRACT FROM AUTHOR]

Satellite data records of stratospheric water vapour have been compared to balloon-borne frost point hygrometer (FP) profiles that are coincident in space and time. The satellite data records of 15 different instruments cover water vapour data available from January 2000 through December 2016. The hygrometer data are from 27 stations all over the world in the same period. For the comparison, real or constructed averaging kernels have been applied to the hygrometer profiles to adjust them to the measurement characteristics of the satellite instruments. For bias evaluation, we have compared satellite profiles averaged over the available temporal coverage to the means of coincident FP profiles for individual stations. For drift determinations, we analysed time series of relative differences between spatiotemporally coincident satellite and hygrometer profiles at individual stations. In a synopsis we have also calculated the mean biases and drifts (and their respective uncertainties) for each satellite record over all applicable hygrometer stations in three altitude ranges (10–30 hPa, 30–100 hPa, and 100 hPa to tropopause). Most of the satellite data have biases <10 % and average drifts <1 % yr -1 in at least one of the respective altitude ranges. Virtually all biases are significant in the sense that their uncertainty range in terms of twice the standard error of the mean does not include zero. Statistically significant drifts (95 % confidence) are detected for 35 % of the ≈ 1200 time series of relative differences between satellites and hygrometers. [ABSTRACT FROM AUTHOR]

The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates GPROF V7 and multiple configurations of the GPROF-NN retrieval against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable precipitation estimates over CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reproduces the principal precipitation characteristics of each region. The effective resolution of GPROF V7 is found to be 51 km over CONUS and 18 km over the tropical Pacific. GPROF V7 produces robust precipitation estimates also for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPROF precipi- tation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error by about twenty percent for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar/radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements comparged to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used in the training of the retrievals for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals. [ABSTRACT FROM AUTHOR]