Articles | Volume 18, issue 12
https://doi.org/10.5194/amt-18-2751-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-2751-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jun 2025
Research article |
| 27 Jun 2025
| 27 Jun 2025
Assessment and application of melting-layer simulations for spaceborne radars within the RTTOV-SCATT v13.1 model
Rohit Mangla
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
now at: Centre for Remote Imaging, Sensing and Processing, National University of Singapore, Singapore, Singapore
Mary Borderies
CORRESPONDING AUTHOR
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Philippe Chambon
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Alan Geer
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
James Hocking
Met Office, Fitzroy Rd., Exeter, UK
Related authors
Rohit Mangla and Indu Jayaluxmi
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-319, https://doi.org/10.5194/amt-2018-319, 2018
Revised manuscript not accepted
Ethel Villeneuve, Philippe Chambon, and Nadia Fourrié
Atmos. Meas. Tech., 17, 3567–3582, https://doi.org/10.5194/amt-17-3567-2024, https://doi.org/10.5194/amt-17-3567-2024, 2024
Short summary
Short summary
In cloudy situations, infrared and microwave observations are complementary, with infrared being sensitive to cloud tops and microwave sensitive to precipitation. However, infrared satellite observations are underused. This study aims to quantify if the inconsistencies in the modelling of clouds prevent the use of cloudy infrared observations in the process of weather forecasting. It shows that the synergistic use of infrared and microwave observations is beneficial, despite inconsistencies.
Chloé Radice, Hélène Brogniez, Pierre-Emmanuel Kirstetter, and Philippe Chambon
Atmos. Chem. Phys., 22, 3811–3825, https://doi.org/10.5194/acp-22-3811-2022, https://doi.org/10.5194/acp-22-3811-2022, 2022
Short summary
Short summary
A novel probabilistic approach is proposed to evaluate relative humidity (RH) profiles simulated by an atmospheric model with respect to satellite-based RH defined from probability distributions. It improves upon deterministic comparisons by enhancing the information content to enable a finer assessment of each model–observation discrepancy, highlighting significant departures within a deterministic confidence range. Geographical and vertical distributions of the model biases are discussed.
Alan J. Geer, Peter Bauer, Katrin Lonitz, Vasileios Barlakas, Patrick Eriksson, Jana Mendrok, Amy Doherty, James Hocking, and Philippe Chambon
Geosci. Model Dev., 14, 7497–7526, https://doi.org/10.5194/gmd-14-7497-2021, https://doi.org/10.5194/gmd-14-7497-2021, 2021
Short summary
Short summary
Satellite observations of radiation from the earth can have strong sensitivity to cloud and precipitation in the atmosphere, with applications in weather forecasting and the development of models. Computing the radiation received at the satellite sensor using radiative transfer theory requires a simulation of the optical properties of a volume containing a large number of cloud and precipitation particles. This article describes the physics used to generate these
bulkoptical properties.
Alan J. Geer
Atmos. Meas. Tech., 14, 5369–5395, https://doi.org/10.5194/amt-14-5369-2021, https://doi.org/10.5194/amt-14-5369-2021, 2021
Short summary
Short summary
Satellite observations sensitive to cloud and precipitation help improve the quality of weather forecasts. However, they are sensitive to things that models do not forecast, such as the shapes and sizes of snow and ice particles. These details can be estimated from the observations themselves and then incorporated in the satellite simulators used in weather forecasting. This approach, known as parameter estimation, will be increasingly useful to build models of poorly known physical processes.
Alistair Bell, Pauline Martinet, Olivier Caumont, Benoît Vié, Julien Delanoë, Jean-Charles Dupont, and Mary Borderies
Atmos. Meas. Tech., 14, 4929–4946, https://doi.org/10.5194/amt-14-4929-2021, https://doi.org/10.5194/amt-14-4929-2021, 2021
Short summary
Short summary
This paper presents work towards making retrievals on the liquid water content in fog and low clouds. Future retrievals will rely on a radar simulator and high-resolution forecast. In this work, real observations are used to assess the errors associated with the simulator and forecast. A selection method to reduce errors associated with the forecast is proposed. It is concluded that the distribution of errors matches the requirements for future retrievals.
James Hocking, Jérôme Vidot, Pascal Brunel, Pascale Roquet, Bruna Silveira, Emma Turner, and Cristina Lupu
Geosci. Model Dev., 14, 2899–2915, https://doi.org/10.5194/gmd-14-2899-2021, https://doi.org/10.5194/gmd-14-2899-2021, 2021
Short summary
Short summary
RTTOV is a fast radiative transfer model for simulating passive satellite-based observations at visible, infrared, and microwave wavelengths. A core part of the model is a parameterisation of the absorption of radiation by the various gases present in the atmosphere. We present a new parameterisation that performs well compared to the existing one in terms of accuracy and can be developed further more easily. The new parameterisation is implemented in the latest release, RTTOV v13.0.
Vasileios Barlakas, Alan J. Geer, and Patrick Eriksson
Atmos. Meas. Tech., 14, 3427–3447, https://doi.org/10.5194/amt-14-3427-2021, https://doi.org/10.5194/amt-14-3427-2021, 2021
Short summary
Short summary
Oriented nonspherical ice particles induce polarization that is ignored when cloud-sensitive satellite observations are used in numerical weather prediction systems. We present a simple approach for approximating particle orientation, requiring minor adaption of software and no additional calculation burden. With this approach, the system realistically simulates the observed polarization patterns, increasing the physical consistency between instruments with different polarizations.
Alan J. Geer, Stefano Migliorini, and Marco Matricardi
Atmos. Meas. Tech., 12, 4903–4929, https://doi.org/10.5194/amt-12-4903-2019, https://doi.org/10.5194/amt-12-4903-2019, 2019
Short summary
Short summary
Satellite radiance observations have only recently become usable in conditions of cloud and precipitation for the initialization of weather forecasts. The move to
all-skyassimilation started with data from the microwave part of the spectrum, with substantial benefit to the quality of operational forecasts. The current work shows a framework in which cloudy infrared data, with its stronger and more non-linear sensitivity, can also benefit operational-quality forecasts.
Alan J. Geer
Atmos. Meas. Tech., 12, 3629–3657, https://doi.org/10.5194/amt-12-3629-2019, https://doi.org/10.5194/amt-12-3629-2019, 2019
Short summary
Short summary
Using more satellite data in cloudy areas helps improve weather forecasts, but all-sky assimilation is still tricky, particularly for infrared data. To allow the use of hyperspectral infrared sounder radiances in all-sky conditions, an error model is developed that, in the presence of cloud, broadens the correlations between channels and increases error variances. After fixing problems of gravity wave and bias amplification, the results of all-sky assimilation trials were promising.
Domenico Cimini, James Hocking, Francesco De Angelis, Angela Cersosimo, Francesco Di Paola, Donatello Gallucci, Sabrina Gentile, Edoardo Geraldi, Salvatore Larosa, Saverio Nilo, Filomena Romano, Elisabetta Ricciardelli, Ermann Ripepi, Mariassunta Viggiano, Lorenzo Luini, Carlo Riva, Frank S. Marzano, Pauline Martinet, Yun Young Song, Myoung Hwan Ahn, and Philip W. Rosenkranz
Geosci. Model Dev., 12, 1833–1845, https://doi.org/10.5194/gmd-12-1833-2019, https://doi.org/10.5194/gmd-12-1833-2019, 2019
Short summary
Short summary
The fast radiative transfer model RTTOV-gb was developed to foster ground-based microwave radiometer data assimilation into numerical weather prediction models, as introduced in a companion paper (https://doi.org/10.5194/gmd-9-2721-2016). Here we present the updates and new features of the current version (v1.0), which is freely accessible online.
Mary Borderies, Olivier Caumont, Julien Delanoë, Véronique Ducrocq, Nadia Fourrié, and Pascal Marquet
Nat. Hazards Earth Syst. Sci., 19, 907–926, https://doi.org/10.5194/nhess-19-907-2019, https://doi.org/10.5194/nhess-19-907-2019, 2019
Short summary
Short summary
The potential of W-band radar reflectivity to improve the quality of analyses and forecasts of heavy precipitation events in the Mediterranean area is investigated. The 1D + 3DVar assimilation method has been adapted to assimilate the W-band reflectivity in the Météo-France kilometre-scale NWP model AROME. The results suggest that the joint assimilation of W-band reflectivity and horizontal wind profiles lead to a slight improvement of moisture analyses and rainfall precipitation forecasts.
Mary Borderies, Olivier Caumont, Julien Delanoë, Véronique Ducrocq, and Nadia Fourrié
Nat. Hazards Earth Syst. Sci., 19, 821–835, https://doi.org/10.5194/nhess-19-821-2019, https://doi.org/10.5194/nhess-19-821-2019, 2019
Short summary
Short summary
The study reports on the impact of the assimilation of wind data from airborne Doppler cloud-profiling radar in a kilometre-scale NWP model on predicting heavy precipitation events in the Mediterranean area. The positive impact of the assimilation of such data is particularly evidenced for a heavy precipitation event and results are slightly encouraging over a 45-day period. In addition, the impact of the length of the assimilation window in a 3h-3DVar assimilation system is investigated.
Katrin Lonitz and Alan J. Geer
Atmos. Meas. Tech., 12, 405–429, https://doi.org/10.5194/amt-12-405-2019, https://doi.org/10.5194/amt-12-405-2019, 2019
Short summary
Short summary
Permittivity models for microwave frequencies of liquid water below 0°C are poorly constrained due to limited laboratory experiments and observations, especially for high microwave frequencies. This uncertainty translates directly into errors in retrieved liquid water paths of up to 80 %. This study investigates the effect of different liquid water permittivity models including models based on the most recent observations.
Fabien Carminati, Stefano Migliorini, Bruce Ingleby, William Bell, Heather Lawrence, Stuart Newman, James Hocking, and Andrew Smith
Atmos. Meas. Tech., 12, 83–106, https://doi.org/10.5194/amt-12-83-2019, https://doi.org/10.5194/amt-12-83-2019, 2019
Short summary
Short summary
The GRUAN processor is a software developed to collocate radiosonde profiles and numerical weather prediction model fields, simulate top-of-atmosphere brightness temperature at frequencies used by space-borne instruments, and propagate the radiosonde uncertainties in that simulation. This work responds to an identified lack of metrologically traceable characterisation of uncertainties in model fields that are increasingly used for the validation and calibration of space-borne instruments.
Rohit Mangla and Indu Jayaluxmi
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-319, https://doi.org/10.5194/amt-2018-319, 2018
Revised manuscript not accepted
Roger Saunders, James Hocking, Emma Turner, Peter Rayer, David Rundle, Pascal Brunel, Jerome Vidot, Pascale Roquet, Marco Matricardi, Alan Geer, Niels Bormann, and Cristina Lupu
Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, https://doi.org/10.5194/gmd-11-2717-2018, 2018
Short summary
Short summary
This paper describes a fast atmospheric radiative transfer model, RTTOV, which is widely used in the satellite retrieval and weather forecast model assimilation communities. It computes top-of-atmosphere radiances for visible, infrared and microwave downward-viewing satellite radiometers. It enables the satellite data, which are a key part of the observing system, to be optimally used with forecast models. The developments made to RTTOV over the past 20 years are summarised.
Francesco De Angelis, Domenico Cimini, Ulrich Löhnert, Olivier Caumont, Alexander Haefele, Bernhard Pospichal, Pauline Martinet, Francisco Navas-Guzmán, Henk Klein-Baltink, Jean-Charles Dupont, and James Hocking
Atmos. Meas. Tech., 10, 3947–3961, https://doi.org/10.5194/amt-10-3947-2017, https://doi.org/10.5194/amt-10-3947-2017, 2017
Short summary
Short summary
Modern data assimilation systems require knowledge of the typical differences between observations and model background (O–B). This work illustrates a 1-year O–B analysis for ground-based microwave radiometer (MWR) observations in clear-sky conditions for a prototype network of six MWRs in Europe. Observations are MWR brightness temperatures (TB). Background profiles extracted from the output of a convective-scale model are used to simulate TB through the radiative transfer model RTTOV-gb.
John C. Kealy, Franco Marenco, John H. Marsham, Luis Garcia-Carreras, Pete N. Francis, Michael C. Cooke, and James Hocking
Atmos. Chem. Phys., 17, 5789–5807, https://doi.org/10.5194/acp-17-5789-2017, https://doi.org/10.5194/acp-17-5789-2017, 2017
Short summary
Short summary
Using novel methods of cloud detection from aircraft data over the Sahara desert, we evaluate the performance of the Meteosat satellite in measuring cloud properties: namely, the cloud mask and the cloud-top height. We find that the cloud mask can justifiably be used for many applications (such as creating a detailed Saharan cloud climatology), and we also discuss its limitations. As for the cloud-top height, we show that the dataset cannot yet be considered robust in this part of the world.
Francesco De Angelis, Domenico Cimini, James Hocking, Pauline Martinet, and Stefan Kneifel
Geosci. Model Dev., 9, 2721–2739, https://doi.org/10.5194/gmd-9-2721-2016, https://doi.org/10.5194/gmd-9-2721-2016, 2016
Short summary
Short summary
Ground-based microwave radiometers (MWRs) offer to bridge the observational gap in the atmospheric boundary layer. Currently MWRs are operational at many sites worldwide. However, their potential is largely under-exploited, partly due to the lack of a fast radiative transfer model (RTM) suited for data assimilation into numerical weather prediction models. Here we propose and test an RTM, building on satellite heritage and adapting for ground-based MWRs, which addresses this shortage.
Hélène Brogniez, Stephen English, Jean-François Mahfouf, Andreas Behrendt, Wesley Berg, Sid Boukabara, Stefan Alexander Buehler, Philippe Chambon, Antonia Gambacorta, Alan Geer, William Ingram, E. Robert Kursinski, Marco Matricardi, Tatyana A. Odintsova, Vivienne H. Payne, Peter W. Thorne, Mikhail Yu. Tretyakov, and Junhong Wang
Atmos. Meas. Tech., 9, 2207–2221, https://doi.org/10.5194/amt-9-2207-2016, https://doi.org/10.5194/amt-9-2207-2016, 2016
Short summary
Short summary
Because a systematic difference between measurements of water vapor performed by space-borne observing instruments in the microwave spectral domain and their numerical modeling was recently highlighted, this work discusses and gives an overview of the various errors and uncertainties associated with each element in the comparison process. Indeed, the knowledge of absolute errors in any observation of the climate system is key, more specifically because we need to detect small changes.
M. S. Johnston, G. Holl, J. Hocking, S. J. Cooper, and D. Chen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amtd-8-11753-2015, https://doi.org/10.5194/amtd-8-11753-2015, 2015
Preprint withdrawn
A. J. Geer and F. Baordo
Atmos. Meas. Tech., 7, 1839–1860, https://doi.org/10.5194/amt-7-1839-2014, https://doi.org/10.5194/amt-7-1839-2014, 2014
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Validation and Intercomparisons
A method to retrieve mixed-phase cloud vertical structure from airborne lidar
Attribution of riming and aggregation processes by application of the vertical distribution of particle shape (VDPS) and spectral retrieval techniques to cloud radar observations
Evaluating parallax and shadow correction methods for global horizontal irradiance retrievals from Meteosat SEVIRI
Factors limiting contrail detection in satellite imagery
Evaluating spectral cloud effective radius retrievals from the Enhanced MODIS Airborne Simulator (eMAS) during ORACLES
Optimizing cloud optical parameterizations in RTTOV for data assimilation of satellite visible reflectance data: an assessment using observed and synthetic images
Errors in stereoscopic retrievals of cloud top height for single-layer clouds
Consideration of the cloud motion for aircraft-based stereographically derived cloud geometry and cloud top heights
Exploring the characteristics of Fengyun-4A Advanced Geostationary Radiation Imager (AGRI) visible reflectance using the China Meteorological Administration Mesoscale (CMA-MESO) forecasts and its implications for data assimilation
Validating global horizontal irradiance retrievals from Meteosat SEVIRI at increased spatial resolution against a dense network of ground-based observations
Effectiveness of Cirrus Detection with MODIS Cloud Mask data
Synergistic approach of frozen hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework
An evaluation of microphysics in a numerical model using Doppler velocity measured by ground-based radar for application to the EarthCARE satellite
Investigation of cirrus cloud properties in the tropical tropopause layer using high-altitude limb-scanning near-IR spectroscopy during NASA-ATTREX
Comparing FY-2F/CTA products to ground-based manual total cloud cover observations in Xinjiang under complex underlying surfaces and different weather conditions
Model-based evaluation of cloud geometry and droplet size retrievals from two-dimensional polarized measurements of specMACS
Improved RepVGG ground-based cloud image classification with attention convolution
An intercomparison of EarthCARE cloud, aerosol, and precipitation retrieval products
First results of cloud retrieval from the Geostationary Environmental Monitoring Spectrometer
Thundercloud structures detected and analyzed based on coherent Doppler wind lidar
Assessing Arctic low-level clouds and precipitation from above – a radar perspective
What CloudSat cannot see: liquid water content profiles inferred from MODIS and CALIOP observations
Validation of the Cloud_CCI (Cloud Climate Change Initiative) cloud products in the Arctic
The Education and Research 3D Radiative Transfer Toolbox (EaR3T) – towards the mitigation of 3D bias in airborne and spaceborne passive imagery cloud retrievals
Retrieval of microphysical parameters of monsoonal rain using X-band dual-polarization radar: their seasonal dependence and evaluation
Consistency test of precipitating ice cloud retrieval properties obtained from the observations of different instruments operating at Dome C (Antarctica)
Sizing ice hydrometeor populations using the dual-wavelength radar ratio
Impact of the revisit frequency on cloud climatology for CALIPSO, EarthCARE, Aeolus, and ICESat-2 satellite lidar missions
The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data
Horizontal geometry of trade wind cumuli – aircraft observations from a shortwave infrared imager versus a radar profiler
Evaluating the consistency and continuity of pixel-scale cloud property data records from Aqua and SNPP (Suomi National Polar-orbiting Partnership)
Quality assessment of Second-generation Global Imager (SGLI)-observed cloud properties using SKYNET surface observation data
Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles
Evaluation of convective cloud microphysics in numerical weather prediction models with dual-wavelength polarimetric radar observations: methods and examples
Synergistic radar and sub-millimeter radiometer retrievals of ice hydrometeors in mid-latitude frontal cloud systems
Evaluation of satellite retrievals of liquid clouds from the GOES-13 imager and MODIS over the midlatitude North Atlantic during the NAAMES campaign
Evaluation of Visible Infrared Imaging Radiometer Suite (VIIRS) neural network cloud detection against current operational cloud masks
The effect of low-level thin arctic clouds on shortwave irradiance: evaluation of estimates from spaceborne passive imagery with aircraft observations
Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O2–O2, MODIS, and Suomi-NPP VIIRS
Dissecting effects of orbital drift of polar-orbiting satellites on accuracy and trends of climate data records of cloud fractional cover
Calibration of global MODIS cloud amount using CALIOP cloud profiles
Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products
An extended radar relative calibration adjustment (eRCA) technique for higher-frequency radars and range–height indicator (RHI) scans
Comparing lightning observations of the ground-based European lightning location system EUCLID and the space-based Lightning Imaging Sensor (LIS) on the International Space Station (ISS)
Microwave and submillimeter wave scattering of oriented ice particles
Shallow cumuli cover and its uncertainties from ground-based lidar–radar data and sky images
Using passive and active observations at microwave and sub-millimetre wavelengths to constrain ice particle models
Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar
Cross-comparison of cloud liquid water path derived from observations by two space-borne and one ground-based instrument in northern Europe
The impact of neglecting ice phase on cloud optical depth retrievals from AERONET cloud mode observations
Ewan Crosbie, Johnathan W. Hair, Amin R. Nehrir, Richard A. Ferrare, Chris Hostetler, Taylor Shingler, David Harper, Marta Fenn, James Collins, Rory Barton-Grimley, Brian Collister, K. Lee Thornhill, Christiane Voigt, Simon Kirschler, and Armin Sorooshian
Atmos. Meas. Tech., 18, 2639–2658, https://doi.org/10.5194/amt-18-2639-2025, https://doi.org/10.5194/amt-18-2639-2025, 2025
Short summary
Short summary
A method was developed to extract information from airborne lidar observations about the distribution of ice and liquid water within clouds. The method specifically targets signatures of horizontal and vertical gradients in ice and water that appear in the polarization of the lidar signals. The method was tested against direct measurements of the cloud properties collected by a second aircraft.
Audrey Teisseire, Anne-Claire Billault-Roux, Teresa Vogl, and Patric Seifert
Atmos. Meas. Tech., 18, 1499–1517, https://doi.org/10.5194/amt-18-1499-2025, https://doi.org/10.5194/amt-18-1499-2025, 2025
Short summary
Short summary
This study demonstrates the ability of a new method delivering the vertical distribution of particle shape to highlight riming and aggregation processes, identifying graupel and aggregates, respectively, as isometric particles. The distinction between these processes can be achieved using lidar or spectral techniques, as demonstrated in the case studies. The capability of the new method to identify rimed particles and aggregates without differentiating them can simplify statistical work.
Job I. Wiltink, Hartwig Deneke, Chiel C. van Heerwaarden, and Jan Fokke Meirink
EGUsphere, https://doi.org/10.5194/egusphere-2024-4139, https://doi.org/10.5194/egusphere-2024-4139, 2025
Short summary
Short summary
Global horizontal irradiance retrievals from satellite observations are affected by spatial displacements due to parallax and cloud shadows. We assess different approaches to correct for these displacements and quantify their added value by comparison with a network of ground-based pyranometer observations. The corrections are found to become increasingly important at higher spatial resolutions and are most relevant for variable cloud types.
Oliver G. A. Driver, Marc E. J. Stettler, and Edward Gryspeerdt
Atmos. Meas. Tech., 18, 1115–1134, https://doi.org/10.5194/amt-18-1115-2025, https://doi.org/10.5194/amt-18-1115-2025, 2025
Short summary
Short summary
Contrails (clouds caused by planes) play a large role in the climate warming caused by aviation. Satellites are a good tool to validate modelled impact estimates. Many contrails are either too narrow or too disperse to detect. This work shows that only around half of contrails are observable but that the most climatically important are easier to detect. It supports the use of satellites for contrail observation but highlights the need for observability considerations for specific applications.
Kerry Meyer, Steven Platnick, G. Thomas Arnold, Nandana Amarasinghe, Daniel Miller, Jennifer Small-Griswold, Mikael Witte, Brian Cairns, Siddhant Gupta, Greg McFarquhar, and Joseph O'Brien
Atmos. Meas. Tech., 18, 981–1011, https://doi.org/10.5194/amt-18-981-2025, https://doi.org/10.5194/amt-18-981-2025, 2025
Short summary
Short summary
Satellite remote sensing retrievals of cloud droplet size are used to understand clouds and their interactions with aerosols and radiation but require many simplifying assumptions. Evaluation of these retrievals is typically done by comparing against direct measurements of droplets from airborne cloud probes. This paper details an evaluation of proxy airborne remote sensing droplet size retrievals against several cloud probes and explores the impact of key assumptions on retrieval agreement.
Yongbo Zhou, Tianrui Cao, and Lijian Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2025-242, https://doi.org/10.5194/egusphere-2025-242, 2025
Short summary
Short summary
Different Cloud Optical Parameterizations (COPs) in RTTOV are evaluated by the comparing the observed and synthetic satellite images. The optimal COP for liquid water cloud is parameterized in terms of effective diameter (Deff) in combination with the COP for ice cloud developed by the Space Science and Engineering Center (SSEC), with the Deff for ice cloud parameterized in terms of ice water content and temperature. The findings will benefit the remote sensing and data assimilation community.
Jesse Loveridge and Larry Di Girolamo
EGUsphere, https://doi.org/10.5194/egusphere-2025-20, https://doi.org/10.5194/egusphere-2025-20, 2025
Short summary
Short summary
Stereoscopic techniques are useful for measuring cloud geometry from space. However, clouds are transparent and often have tenuous boundaries. We evaluate the effect of this using numerical simulations. We find that stereoscopy tends to retrieve a cloud boundary that is interior to the true shape by around 100 m and is smoother, depending on the cloud shape and resolution of the instrument. This error is similar across views, highlighting the strength of stereoscopy for change detection.
Lea Volkmer, Tobias Kölling, Tobias Zinner, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6807–6817, https://doi.org/10.5194/amt-17-6807-2024, https://doi.org/10.5194/amt-17-6807-2024, 2024
Short summary
Short summary
The importance of the consideration of cloud motion for the stereographic determination of cloud top height from aircraft observations is demonstrated using measurements of the airborne spectrometer of the Munich Aerosol Cloud Scanner (specMACS). A method for cloud motion correction using model winds from the European Centre for Medium-Range Weather Forecasts is presented and validated using both real measurements and realistic radiative transfer simulations.
Yongbo Zhou, Yubao Liu, Wei Han, Yuefei Zeng, Haofei Sun, Peilong Yu, and Lijian Zhu
Atmos. Meas. Tech., 17, 6659–6675, https://doi.org/10.5194/amt-17-6659-2024, https://doi.org/10.5194/amt-17-6659-2024, 2024
Short summary
Short summary
The study explored differences between the visible reflectance provided by the Fengyun-4A satellite and its equivalent derived from the China Meteorological Administration Mesoscale model using a forward operator. The observation-minus-simulation biases were able to monitor the performance of the satellite visible instrument. The biases were corrected based on a first-order approximation method, which promotes the data assimilation of satellite visible reflectance in real-world cases.
Job I. Wiltink, Hartwig Deneke, Yves-Marie Saint-Drenan, Chiel C. van Heerwaarden, and Jan Fokke Meirink
Atmos. Meas. Tech., 17, 6003–6024, https://doi.org/10.5194/amt-17-6003-2024, https://doi.org/10.5194/amt-17-6003-2024, 2024
Short summary
Short summary
Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) global horizontal irradiance (GHI) retrievals are validated at standard and increased spatial resolution against a network of 99 pyranometers. GHI accuracy is strongly dependent on the cloud regime. Days with variable cloud conditions show significant accuracy improvements when retrieved at higher resolution. We highlight the benefits of dense network observations and a cloud-regime-resolved approach in validating GHI retrievals.
Żaneta Nguyen Huu, Andrzej Z. Kotarba, and Agnieszka Wypych
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-163, https://doi.org/10.5194/amt-2024-163, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
Clouds affect Earth's energy balance, with high-altitude cirrus clouds contributing to atmospheric warming. While active satellite sensors are the most accurate for detecting cirrus clouds, they are not ideal for long-term studies. This study compares MODIS and CALIOP data, testing six MODIS methods and two ISCCP tests. The ATC test was the most effective, achieving 72.98 % accuracy during daytime and 59.50 % at night, making it suitable for creating a high-level cloud mask.
Ethel Villeneuve, Philippe Chambon, and Nadia Fourrié
Atmos. Meas. Tech., 17, 3567–3582, https://doi.org/10.5194/amt-17-3567-2024, https://doi.org/10.5194/amt-17-3567-2024, 2024
Short summary
Short summary
In cloudy situations, infrared and microwave observations are complementary, with infrared being sensitive to cloud tops and microwave sensitive to precipitation. However, infrared satellite observations are underused. This study aims to quantify if the inconsistencies in the modelling of clouds prevent the use of cloudy infrared observations in the process of weather forecasting. It shows that the synergistic use of infrared and microwave observations is beneficial, despite inconsistencies.
Woosub Roh, Masaki Satoh, Yuichiro Hagihara, Hiroaki Horie, Yuichi Ohno, and Takuji Kubota
Atmos. Meas. Tech., 17, 3455–3466, https://doi.org/10.5194/amt-17-3455-2024, https://doi.org/10.5194/amt-17-3455-2024, 2024
Short summary
Short summary
The advantage of the use of Doppler velocity in the categorization of the hydrometeors is that Doppler velocities suffer less impact from the attenuation of rain and wet attenuation on an antenna. The ground Cloud Profiling Radar observation of the radar reflectivity for the precipitation case is limited because of wet attenuation on an antenna. We found the main contribution to Doppler velocities is the terminal velocity of hydrometeors by analysis of simulation results.
Santo Fedele Colosimo, Nathaniel Brockway, Vijay Natraj, Robert Spurr, Klaus Pfeilsticker, Lisa Scalone, Max Spolaor, Sarah Woods, and Jochen Stutz
Atmos. Meas. Tech., 17, 2367–2385, https://doi.org/10.5194/amt-17-2367-2024, https://doi.org/10.5194/amt-17-2367-2024, 2024
Short summary
Short summary
Cirrus clouds are poorly understood components of the climate system, in part due to the challenge of observing thin, sub-visible ice clouds. We address this issue with a new observational approach that uses the remote sensing of near-infrared ice water absorption features from a high-altitude aircraft. We describe the underlying principle of this approach and present a new procedure to retrieve ice concentration in cirrus clouds. Our retrievals compare well with in situ observations.
Shuai Li, Hua Zhang, Yonghang Chen, Zhili Wang, Xiangyu Li, Yuan Li, and Yuanyuan Xue
Atmos. Meas. Tech., 17, 2011–2024, https://doi.org/10.5194/amt-17-2011-2024, https://doi.org/10.5194/amt-17-2011-2024, 2024
Short summary
Short summary
In this paper, Xinjiang was the test area, and nine evaluation indexes of FY-2F/CTA, including precision rate, false rate, missing rate, consistency rate, strong rate, weak rate, bias, AE, and RMSE, were calculated and analyzed under complex underlying surface (subsurface types, temperature and altitude conditions) and different weather conditions (dust effects and different cloud cover levels). The precision, consistency, and error indexes of FY-2F/CTA were tested and evaluated.
Lea Volkmer, Veronika Pörtge, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 17, 1703–1719, https://doi.org/10.5194/amt-17-1703-2024, https://doi.org/10.5194/amt-17-1703-2024, 2024
Short summary
Short summary
Three-dimensional radiative transfer simulations are used to evaluate the performance of retrieval algorithms in the derivation of cloud geometry (cloud top heights) and cloud droplet size distributions from two-dimensional polarized radiance measurements of the airborne spectrometer of the Munich Aerosol Cloud Scanner. The cloud droplet size distributions are derived for the effective radius and variance. The simulations are based on cloud data from highly resolved large-eddy simulations.
Chaojun Shi, Leile Han, Ke Zhang, Hongyin Xiang, Xingkuan Li, Zibo Su, and Xian Zheng
Atmos. Meas. Tech., 17, 979–997, https://doi.org/10.5194/amt-17-979-2024, https://doi.org/10.5194/amt-17-979-2024, 2024
Short summary
Short summary
This article mainly studies the problem of ground cloud classification and significantly improves the accuracy of ground cloud classification by applying an improved deep-learning method. The research results show that the method proposed in this article has a significant impact on the classification results of ground cloud images. These conclusions have important implications for providing new insights and future research directions in the field of ground cloud classification.
Shannon L. Mason, Howard W. Barker, Jason N. S. Cole, Nicole Docter, David P. Donovan, Robin J. Hogan, Anja Hünerbein, Pavlos Kollias, Bernat Puigdomènech Treserras, Zhipeng Qu, Ulla Wandinger, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, https://doi.org/10.5194/amt-17-875-2024, 2024
Short summary
Short summary
When the EarthCARE mission enters its operational phase, many retrieval data products will be available, which will overlap both in terms of the measurements they use and the geophysical quantities they report. In this pre-launch study, we use simulated EarthCARE scenes to compare the coverage and performance of many data products from the European Space Agency production model, with the intention of better understanding the relation between products and providing a compact guide to users.
Bo-Ram Kim, Gyuyeon Kim, Minjeong Cho, Yong-Sang Choi, and Jhoon Kim
Atmos. Meas. Tech., 17, 453–470, https://doi.org/10.5194/amt-17-453-2024, https://doi.org/10.5194/amt-17-453-2024, 2024
Short summary
Short summary
This study introduces the GEMS cloud algorithm and validates its results using data from GEMS and other environmental satellites. The GEMS algorithm is able to detect the lowest cloud heights among the four satellites, and its effective cloud fraction and cloud centroid pressure are well reflected in the retrieval results. The study highlights the algorithm's usefulness in correcting errors in trace gases caused by clouds in the East Asian region.
Kenan Wu, Tianwen Wei, Jinlong Yuan, Haiyun Xia, Xin Huang, Gaopeng Lu, Yunpeng Zhang, Feifan Liu, Baoyou Zhu, and Weidong Ding
Atmos. Meas. Tech., 16, 5811–5825, https://doi.org/10.5194/amt-16-5811-2023, https://doi.org/10.5194/amt-16-5811-2023, 2023
Short summary
Short summary
A compact all-fiber coherent Doppler wind lidar (CDWL) working at the 1.5 µm wavelength is applied to probe the dynamics and microphysics structure of thunderstorms. It was found that thunderclouds below the 0 ℃ isotherm have significant spectrum broadening and an increase in skewness, and that lightning affects the microphysics structure of the thundercloud. It is proven that the precise spectrum of CDWL is a promising indicator for studying the charge structure of thunderstorms.
Imke Schirmacher, Pavlos Kollias, Katia Lamer, Mario Mech, Lukas Pfitzenmaier, Manfred Wendisch, and Susanne Crewell
Atmos. Meas. Tech., 16, 4081–4100, https://doi.org/10.5194/amt-16-4081-2023, https://doi.org/10.5194/amt-16-4081-2023, 2023
Short summary
Short summary
CloudSat’s relatively coarse spatial resolution, low sensitivity, and blind zone limit its assessment of Arctic low-level clouds, which affect the surface energy balance. We compare cloud fractions from CloudSat and finely resolved airborne radar observations to determine CloudSat’s limitations. Cloudsat overestimates cloud fractions above its blind zone, especially during cold-air outbreaks over open water, and misses a cloud fraction of 32 % and half of the precipitation inside its blind zone.
Richard M. Schulte, Matthew D. Lebsock, and John M. Haynes
Atmos. Meas. Tech., 16, 3531–3546, https://doi.org/10.5194/amt-16-3531-2023, https://doi.org/10.5194/amt-16-3531-2023, 2023
Short summary
Short summary
In order to constrain climate models and better understand how clouds might change in future climates, accurate satellite estimates of cloud liquid water content are important. The satellite currently best suited to this purpose, CloudSat, is not sensitive enough to detect some non-raining low clouds. In this study we show that information from two other satellite instruments, MODIS and CALIOP, can be combined to provide cloud water estimates for many of the clouds that are missed by CloudSat.
Kameswara S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. Shupe, Kerstin Ebell, and John P. Burrows
Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, https://doi.org/10.5194/amt-16-2903-2023, 2023
Short summary
Short summary
Clouds play an important role in Arctic amplification. Cloud data from ground-based sites are valuable but cannot represent the whole Arctic. Therefore the use of satellite products is a measure to cover the entire Arctic. However, the quality of such cloud measurements from space is not well known. The paper discusses the differences and commonalities between satellite and ground-based measurements. We conclude that the satellite dataset, with a few exceptions, can be used in the Arctic.
Hong Chen, K. Sebastian Schmidt, Steven T. Massie, Vikas Nataraja, Matthew S. Norgren, Jake J. Gristey, Graham Feingold, Robert E. Holz, and Hironobu Iwabuchi
Atmos. Meas. Tech., 16, 1971–2000, https://doi.org/10.5194/amt-16-1971-2023, https://doi.org/10.5194/amt-16-1971-2023, 2023
Short summary
Short summary
We introduce the Education and Research 3D Radiative Transfer Toolbox (EaR3T) and propose a radiance self-consistency approach for quantifying and mitigating 3D bias in legacy airborne and spaceborne imagery retrievals due to spatially inhomogeneous clouds and surfaces.
Kumar Abhijeet, Thota Narayana Rao, Nidamanuri Rama Rao, and Kasimahanthi Amar Jyothi
Atmos. Meas. Tech., 16, 871–888, https://doi.org/10.5194/amt-16-871-2023, https://doi.org/10.5194/amt-16-871-2023, 2023
Short summary
Short summary
The present study focuses on retrieving and validating raindrop size distribution (DSD) relations for monsoonal rainfall, which are required for retrieving DSDs with polarimetric radar measurements. The seasonal variation in DSD is quite large and significant, and as a result the coefficients also vary considerably between the seasons and from those existing elsewhere. Among the existing DSD methods, the N-gamma method performs better than the other methods.
Gianluca Di Natale, David D. Turner, Giovanni Bianchini, Massimo Del Guasta, Luca Palchetti, Alessandro Bracci, Luca Baldini, Tiziano Maestri, William Cossich, Michele Martinazzo, and Luca Facheris
Atmos. Meas. Tech., 15, 7235–7258, https://doi.org/10.5194/amt-15-7235-2022, https://doi.org/10.5194/amt-15-7235-2022, 2022
Short summary
Short summary
In this paper, we describe a new approach to test the consistency of the precipitating ice cloud optical and microphysical properties in Antarctica, Dome C, retrieved from hyperspectral measurements in the far-infrared, with the reflectivity detected by a co-located micro rain radar operating at 24 GHz. The retrieved ice crystal sizes were found in accordance with the direct measurements of an optical imager, also installed at Dome C, which can collect the falling ice particles.
Sergey Y. Matrosov, Alexei Korolev, Mengistu Wolde, and Cuong Nguyen
Atmos. Meas. Tech., 15, 6373–6386, https://doi.org/10.5194/amt-15-6373-2022, https://doi.org/10.5194/amt-15-6373-2022, 2022
Short summary
Short summary
A remote sensing method to retrieve sizes of particles in ice clouds and precipitation from radar measurements at two wavelengths is described. This method is based on relating the particle size information to the ratio of radar signals at these two wavelengths. It is demonstrated that this ratio is informative about different characteristic particle sizes. Knowing atmospheric ice particle sizes is important for many applications such as precipitation estimation and climate modeling.
Andrzej Z. Kotarba
Atmos. Meas. Tech., 15, 4307–4322, https://doi.org/10.5194/amt-15-4307-2022, https://doi.org/10.5194/amt-15-4307-2022, 2022
Short summary
Short summary
Space profiling lidars offer a unique insight into cloud properties in Earth’s atmosphere, and are considered the most reliable source of cloud information. However, lidar-based cloud climatologies are infrequently sampled: every 7 to 91 d, and only along the ground track. This study evaluated how accurate are the cloud data from existing (CALIPSO, ICESat-2, Aeolus) and planned (EarthCARE) space lidars, when compared to a cloud climatology obtained with observations taken every day.
Edward Gryspeerdt, Daniel T. McCoy, Ewan Crosbie, Richard H. Moore, Graeme J. Nott, David Painemal, Jennifer Small-Griswold, Armin Sorooshian, and Luke Ziemba
Atmos. Meas. Tech., 15, 3875–3892, https://doi.org/10.5194/amt-15-3875-2022, https://doi.org/10.5194/amt-15-3875-2022, 2022
Short summary
Short summary
Droplet number concentration is a key property of clouds, influencing a variety of cloud processes. It is also used for estimating the cloud response to aerosols. The satellite retrieval depends on a number of assumptions – different sampling strategies are used to select cases where these assumptions are most likely to hold. Here we investigate the impact of these strategies on the agreement with in situ data, the droplet number climatology and estimates of the indirect radiative forcing.
Henning Dorff, Heike Konow, and Felix Ament
Atmos. Meas. Tech., 15, 3641–3661, https://doi.org/10.5194/amt-15-3641-2022, https://doi.org/10.5194/amt-15-3641-2022, 2022
Short summary
Short summary
This study elaborates how aircraft-based horizontal geometries of trade wind cumuli differ whether a one-dimensional profiling radar or a two-dimensional imager is used. Cloud size distributions are examined in terms of sensitivity to sample size, resolution, and instrument field of view. While the radar cannot reproduce the double power law distribution due to coarse resolution and restriction to vertical transects, the imager also reveals the elliptic cloud structure enhancing with wind speed.
Qing Yue, Eric J. Fetzer, Likun Wang, Brian H. Kahn, Nadia Smith, John M. Blaisdell, Kerry G. Meyer, Mathias Schreier, Bjorn Lambrigtsen, and Irina Tkatcheva
Atmos. Meas. Tech., 15, 2099–2123, https://doi.org/10.5194/amt-15-2099-2022, https://doi.org/10.5194/amt-15-2099-2022, 2022
Short summary
Short summary
The self-consistency and continuity of cloud retrievals from infrared sounders and imagers aboard Aqua and SNPP (Suomi National Polar-orbiting Partnership) are examined at the pixel scale. Cloud products are found to be consistent with each other. Differences between sounder products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric retrievals. The impact of algorithm and instrument differences is clearly seen in the imager cloud retrievals.
Pradeep Khatri, Tadahiro Hayasaka, Hitoshi Irie, Husi Letu, Takashi Y. Nakajima, Hiroshi Ishimoto, and Tamio Takamura
Atmos. Meas. Tech., 15, 1967–1982, https://doi.org/10.5194/amt-15-1967-2022, https://doi.org/10.5194/amt-15-1967-2022, 2022
Short summary
Short summary
Cloud properties observed by the Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission – Climate (GCOM-C) satellite are evaluated using surface observation data. The study finds that SGLI-observed cloud properties are qualitative enough, although water cloud properties are suggested to be more qualitative, and both water and ice cloud properties can reproduce surface irradiance quite satisfactorily. Thus, SGLI cloud products are very useful for different studies.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, Rodrigo Guzman, Cyprien Gindre, Po-Lun Ma, and Marjolaine Chiriaco
Atmos. Meas. Tech., 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, https://doi.org/10.5194/amt-15-1055-2022, 2022
Short summary
Short summary
Space-borne lidars have been providing invaluable information of atmospheric optical properties since 2006, and new lidar missions are on the way to ensure continuous observations. In this work, we compare the clouds estimated from space-borne ALADIN and CALIOP lidar observations. The analysis of collocated data shows that the agreement between the retrieved clouds is good up to 3 km height. Above that, ALADIN detects 40 % less clouds than CALIOP, except for polar stratospheric clouds (PSCs).
Gregor Köcher, Tobias Zinner, Christoph Knote, Eleni Tetoni, Florian Ewald, and Martin Hagen
Atmos. Meas. Tech., 15, 1033–1054, https://doi.org/10.5194/amt-15-1033-2022, https://doi.org/10.5194/amt-15-1033-2022, 2022
Short summary
Short summary
We present a setup for systematic characterization of differences between numerical weather models and radar observations for convective weather situations. Radar observations providing dual-wavelength and polarimetric variables to infer information about hydrometeor shapes and sizes are compared against simulations using microphysics schemes of varying complexity. Differences are found in ice and liquid phase, pointing towards issues of some schemes in reproducing particle size distributions.
Simon Pfreundschuh, Stuart Fox, Patrick Eriksson, David Duncan, Stefan A. Buehler, Manfred Brath, Richard Cotton, and Florian Ewald
Atmos. Meas. Tech., 15, 677–699, https://doi.org/10.5194/amt-15-677-2022, https://doi.org/10.5194/amt-15-677-2022, 2022
Short summary
Short summary
We test a novel method to remotely measure ice particles in clouds. This is important because such measurements are required to improve climate and weather models. The method combines a radar with newly developed sensors measuring microwave radiation at very short wavelengths. We use observations made from aircraft flying above the cloud and compare them to real measurements from inside the cloud. This works well given that one can model the ice particles in the cloud sufficiently well.
David Painemal, Douglas Spangenberg, William L. Smith Jr., Patrick Minnis, Brian Cairns, Richard H. Moore, Ewan Crosbie, Claire Robinson, Kenneth L. Thornhill, Edward L. Winstead, and Luke Ziemba
Atmos. Meas. Tech., 14, 6633–6646, https://doi.org/10.5194/amt-14-6633-2021, https://doi.org/10.5194/amt-14-6633-2021, 2021
Short summary
Short summary
Cloud properties derived from satellite sensors are critical for the global monitoring of climate. This study evaluates satellite-based cloud properties over the North Atlantic using airborne data collected during NAAMES. Satellite observations of droplet size and cloud optical depth tend to compare well with NAAMES data. The analysis indicates that the satellite pixel resolution and the specific viewing geometry need to be taken into account in research applications.
Charles H. White, Andrew K. Heidinger, and Steven A. Ackerman
Atmos. Meas. Tech., 14, 3371–3394, https://doi.org/10.5194/amt-14-3371-2021, https://doi.org/10.5194/amt-14-3371-2021, 2021
Short summary
Short summary
Automated detection of clouds in satellite imagery is an important practice that is useful for predicting and understanding both weather and climate. Cloud detection is often difficult at night and over cold surfaces. In this paper, we discuss how a complex statistical model (a neural network) can more accurately detect clouds compared to currently used approaches. Overall, our results suggest that our approach could result in more reliable assessments of global cloud cover.
Hong Chen, Sebastian Schmidt, Michael D. King, Galina Wind, Anthony Bucholtz, Elizabeth A. Reid, Michal Segal-Rozenhaimer, William L. Smith, Patrick C. Taylor, Seiji Kato, and Peter Pilewskie
Atmos. Meas. Tech., 14, 2673–2697, https://doi.org/10.5194/amt-14-2673-2021, https://doi.org/10.5194/amt-14-2673-2021, 2021
Short summary
Short summary
In this paper, we accessed the shortwave irradiance derived from MODIS cloud optical properties by using aircraft measurements. We developed a data aggregation technique to parameterize spectral surface albedo by snow fraction in the Arctic. We found that undetected clouds have the most significant impact on the imagery-derived irradiance. This study suggests that passive imagery cloud detection could be improved through a multi-pixel approach that would make it more dependable in the Arctic.
Steven Compernolle, Athina Argyrouli, Ronny Lutz, Maarten Sneep, Jean-Christopher Lambert, Ann Mari Fjæraa, Daan Hubert, Arno Keppens, Diego Loyola, Ewan O'Connor, Fabian Romahn, Piet Stammes, Tijl Verhoelst, and Ping Wang
Atmos. Meas. Tech., 14, 2451–2476, https://doi.org/10.5194/amt-14-2451-2021, https://doi.org/10.5194/amt-14-2451-2021, 2021
Short summary
Short summary
The high-resolution satellite Sentinel-5p TROPOMI observes several atmospheric gases. To account for cloud interference with the observations, S5P cloud data products (CLOUD OCRA/ROCINN_CAL, OCRA/ROCINN_CRB, and FRESCO) provide vital input: cloud fraction, cloud height, and cloud optical thickness. Here, S5P cloud parameters are validated by comparing with other satellite sensors (VIIRS, MODIS, and OMI) and with ground-based CloudNet data. The agreement depends on product type and cloud height.
Jędrzej S. Bojanowski and Jan P. Musiał
Atmos. Meas. Tech., 13, 6771–6788, https://doi.org/10.5194/amt-13-6771-2020, https://doi.org/10.5194/amt-13-6771-2020, 2020
Short summary
Short summary
Satellites such as NOAA's Advanced Very High Resolution Radiometer can uniquely observe changes in cloud cover but are affected by orbital drift that results in shifted image acquisition times, which in turn lead to spurious trends in cloud cover detected during climatological analyses. Providing a detailed quantification of these trends, we show that climate data records must be analysed with caution, as for some periods and regions they do not comply with the requirements for climate data.
Andrzej Z. Kotarba
Atmos. Meas. Tech., 13, 4995–5012, https://doi.org/10.5194/amt-13-4995-2020, https://doi.org/10.5194/amt-13-4995-2020, 2020
Short summary
Short summary
This paper evaluates the operational approach for producing global (Level 3) cloud amount based on MODIS cloud masks (Level 2). Using CALIPSO we calculate the actual cloud fractions for each cloud mask category, which are 21.5 %, 27.7 %, 66.6 %, and 94.7 % instead of assumed 0 %, 0 %, 100 %, and 100 %. Consequently we find the operational procedure unreliable, especially on a regional/local scale. A method of how to correct and calibrate MODIS global data using CALIPSO detections is suggested.
Benjamin Marchant, Steven Platnick, Kerry Meyer, and Galina Wind
Atmos. Meas. Tech., 13, 3263–3275, https://doi.org/10.5194/amt-13-3263-2020, https://doi.org/10.5194/amt-13-3263-2020, 2020
Short summary
Short summary
Multilayer cloud scenes (such as an ice cloud overlapping a liquid cloud) are common in the Earth's atmosphere and are quite difficult to detect from space. The detection of multilayer clouds is important to better understand how they interact with the light and their impact on the climate. So, for the instrument MODIS an algorithm has been developed to detect those clouds, and this paper presents an evaluation of this algorithm by comparing it with
other instruments.
Alexis Hunzinger, Joseph C. Hardin, Nitin Bharadwaj, Adam Varble, and Alyssa Matthews
Atmos. Meas. Tech., 13, 3147–3166, https://doi.org/10.5194/amt-13-3147-2020, https://doi.org/10.5194/amt-13-3147-2020, 2020
Short summary
Short summary
The calibration of weather radars is one of the most dominant sources of errors hindering their use. This work takes a technique for tracking the changes in radar calibration using the radar clutter from the ground and extends it to higher-frequency research radars. It demonstrates that after modifications the technique is successful but that special care needs to be taken in its application at high frequencies. The technique is verified using data from multiple DOE ARM field campaigns.
Dieter R. Poelman and Wolfgang Schulz
Atmos. Meas. Tech., 13, 2965–2977, https://doi.org/10.5194/amt-13-2965-2020, https://doi.org/10.5194/amt-13-2965-2020, 2020
Short summary
Short summary
The objective of this work is to quantify the similarities and contrasts between the lightning observations from the Lightning Imaging Sensor (LIS) on the International Space Station (ISS) and the ground-based European Cooperation for Lightning Detection (EUCLID) network. This work is timely, given that the Meteosat Third Generation (MTG), which has a lightning imager (LI) on board, is going to be launched in 2 years.
Manfred Brath, Robin Ekelund, Patrick Eriksson, Oliver Lemke, and Stefan A. Buehler
Atmos. Meas. Tech., 13, 2309–2333, https://doi.org/10.5194/amt-13-2309-2020, https://doi.org/10.5194/amt-13-2309-2020, 2020
Short summary
Short summary
Microwave dual-polarization observations consistently show that larger atmospheric ice particles tend to have a preferred orientation. We provide a publicly available database of microwave and submillimeter wave scattering properties of oriented ice particles based on discrete dipole approximation scattering calculations. Detailed radiative transfer simulations, recreating observed polarization patterns, are additionally presented in this study.
Erin A. Riley, Jessica M. Kleiss, Laura D. Riihimaki, Charles N. Long, Larry K. Berg, and Evgueni Kassianov
Atmos. Meas. Tech., 13, 2099–2117, https://doi.org/10.5194/amt-13-2099-2020, https://doi.org/10.5194/amt-13-2099-2020, 2020
Short summary
Short summary
Discrepancies in hourly shallow cumuli cover estimates can be substantial. Instrument detection differences contribute to long-term bias in shallow cumuli cover estimates, whereas narrow field-of-view configurations impact measurement uncertainty as averaging time decreases. A new tool is introduced to visually assess both impacts on sub-hourly cloud cover estimates. Accurate shallow cumuli cover estimation is needed for model–observation comparisons and studying cloud-surface interactions.
Robin Ekelund, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 13, 501–520, https://doi.org/10.5194/amt-13-501-2020, https://doi.org/10.5194/amt-13-501-2020, 2020
Short summary
Short summary
Atmospheric ice particles (e.g. snow and ice crystals) are an important part of weather, climate, and the hydrological cycle. This study investigates whether combined satellite measurements by radar and radiometers at microwave wavelengths can be used to find the most likely shape of such ice particles. The method was limited when using only currently operating sensors (CloudSat radar and the GPM Microwave Imager) but shows promise if the upcoming Ice Cloud Imager is also considered.
Juan Huo, Daren Lu, Shu Duan, Yongheng Bi, and Bo Liu
Atmos. Meas. Tech., 13, 1–11, https://doi.org/10.5194/amt-13-1-2020, https://doi.org/10.5194/amt-13-1-2020, 2020
Short summary
Short summary
Cloud top height (CTH) is one of the important cloud parameters providing information about the vertical structure of cloud water content. To better understand the accuracy of CTH derived from passive satellite data, 2 years of ground-based Ka-band radar measurements are compared with CTH inferred from Terra/Aqua MODIS and Himawari AHI. It is found that MODIS and AHI underestimate CTH relative to radar by −1.10 km. Both MODIS and AHI CTH retrieval accuracy depend strongly on cloud depth.
Vladimir S. Kostsov, Anke Kniffka, Martin Stengel, and Dmitry V. Ionov
Atmos. Meas. Tech., 12, 5927–5946, https://doi.org/10.5194/amt-12-5927-2019, https://doi.org/10.5194/amt-12-5927-2019, 2019
Short summary
Short summary
Cloud liquid water path (LWP) is one of the target atmospheric parameters retrieved remotely from ground-based and space-borne platforms. The LWP data delivered by the satellite instruments SEVIRI and AVHRR together with the data provided by the ground-based radiometer RPG-HATPRO near St. Petersburg, Russia, have been compared. Our study revealed considerable differences between LWP data from SEVIRI and AVHRR in winter over ice-covered relatively small water bodies in this region.
Jonathan K. P. Shonk, Jui-Yuan Christine Chiu, Alexander Marshak, David M. Giles, Chiung-Huei Huang, Gerald G. Mace, Sally Benson, Ilya Slutsker, and Brent N. Holben
Atmos. Meas. Tech., 12, 5087–5099, https://doi.org/10.5194/amt-12-5087-2019, https://doi.org/10.5194/amt-12-5087-2019, 2019
Short summary
Short summary
Retrievals of cloud optical depth made using AERONET radiometers in “cloud mode” rely on the assumption that all cloud is liquid. The presence of ice cloud therefore introduces errors in the retrieved optical depth, which can be over 25 in optically thick ice clouds. However, such clouds are not frequent and the long-term mean optical depth error is about 3 for a sample of real clouds. A correction equation could improve the retrieval further, although this would require extra instrumentation.
Cited articles
Aonashi, K., Battaglia, A., Bolvin, D. T., Borderies, M., Chambon, P., Ferraro, R. R., Geer, A. J., Haddad, Z., Huffman, G. J., Ikuta, Y., Johnson, B. T., Kachi, M., Kidd, C., Kirstetter, P., Kubota, T., Kummerow, C., Louf, V., Maggioni, V., Mangla, R., Okamoto, K., Protat, A., and Shige, S.: A review of the different operational applica- tions of spaceborne precipitation radars within the International Precipitation Working Group (IPWG) community, IPWG, https://www.eorc.jaxa.jp/IPWG/reports/IPWG_review_applications_space-borne_precipitation_radars.pdf (last access: 10 June 2021), 2021. a
Augros, C., Caumont, O., Ducrocq, V., Gaussiat, N., and Tabary, P.: Comparisons between S-, C-and X-band polarimetric radar observations and convective-scale simulations of the HyMeX first special observing period, Q. J. Roy. Meteorol. Soc., 142, 347–362, 2016. a
Battaglia, A., Augustynek, T., Tanelli, S., and Kollias, P.: Multiple scattering identification in spaceborne W-band radar measurements of deep convective cores, J. Geophys. Res.-Atmos., 116, D19201, https://doi.org/10.1029/2011JD016142, 2011. a
Battaglia, A., Kollias, P., Dhillon, R., Roy, R., Tanelli, S., Lamer, K., Grecu, M., Lebsock, M., Watters, D., Mroz, K., Heymsfield, G., Li, L., and Furukawa, K.: Spaceborne cloud and precipitation radars: Status, challenges, and ways forward, Rev. Geophys., 58, e2019RG000686, https://doi.org/10.1029/2019RG000686, 2020. a
Bodas-Salcedo, A., Webb, M. J., Brooks, M. E., Ringer, M. A., William, K. D., Milton, S. F., and Wilson, D. R.: Evaluating cloud systems in the Met Office global forecast model using simulated CloudSat radar reflectivities, J. Geophys. Res., 113, D00A13, https://doi.org/10.1029/2007JD009620, 2008. a, b
Bodas-Salcedo, A., Webb, M. J., Brooks, M. E., Ringer, M. A., William, K. D., Milton, S. F., and Wilson, D. R.: Evaluating cloud systems in the Met Office global forecast model using simulated CloudSat radar reflectivities, J. Geophys. Res., 113, D00A13, https://doi.org/10.1029/2007JD009620, 2008. a
Bodas-Salcedo, A., Webb, M., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S., Zhang, Y., Marchand, R., Haynes, J., Pincus, R., and John, V. O.: COSP: Satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, 2011. a
Bohren, C. F. and Battan, L. J.: Radar backscattering of microwaves by spongy ice spheres, J. Atmos. Sci., 39, 2623–2628, 1982. a
Boodoo, S., Hudak, D., Donaldson, N., and Leduc, M.: Application of dual-polarization radar melting-layer detection algorithm, J. Appl. Meteorol. Climatol., 49, 1779–1793, 2010. a
Borderies, M., Caumont, O., Augros, C., Bresson, É., Delanoë, J., Ducrocq, V., Fourrié, N., Bastard, T. L., and Nuret, M.: Simulation of W-band radar reflectivity for model validation and data assimilation, Q. J. Roy. Meteorol. Soc., 144, 391–403, 2018. a
Botta, G., Aydin, K., and Verlinde, J.: Modeling of microwave scattering from cloud ice crystal aggregates and melting aggregates: A new approach, IEEE Geosci. Remote Sens. Lett., 7, 572–576, 2010. a
Bouyssel, F., Berre, L., Bénichou, H., Chambon, P., Girardot, N., Guidard, V., Loo, C., Mahfouf, J.-F., Moll, P., Payan, C., and Raspaud, D.: The 2020 global operational nwp data assimilation system at météo-france, Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV), 645–664, https://doi.org/10.1007/978-3-030-77722-7_25, 2022. a
Cintineo, R., Otkin, J. A., Xue, M., and Kong, F.: Evaluating the performance of planetary boundary layer and cloud microphysical parameterization schemes in convection-permitting ensemble forecasts using synthetic GOES-13 satellite observations, Mon. Weather Rev., 142, 163–182, 2014. a
Courtier, P.: The ARPEGE Project at Météo-France, in: Proc. of ECMWF seminar on Numerical Methods in Atmospheric Models, 9–13 September 1991, ECMWF: Reading, UK, 1991. a
Das, S. K., Deshpande, S. M., Krishna, U. M., Konwar, M., Kolte, Y. K., Chakravarty, K., Kalapureddy, M., and Sahoo, S.: Aspects of melting layer and fall streaks in stratiform cloud system over the Western Ghats, India from Ka-band polarimetric radar observations, Atmos. Res., 281, 106463, https://doi.org/10.1016/j.atmosres.2022.106463, 2023. a
Desroziers, G., Pouponneau, B., Thépaut, J.-N., Janisková, M., and Veersé, F.: Four-dimensional variational analyses of FASTEX situations using special observations, Q. J. Roy. Meteorol. Soc., 125, 3339–3358, 1999. a
Di Michele, S., Ahlgrimm, M., Forbes, R., Kulie, M., Bennartz, R., Janisková, M., and Bauer, P.: Interpreting an evaluation of the ECMWF global model with CloudSat observations: Ambiguities due to radar reflectivity forward operator uncertainties, Q. J. Roy. Meteorol. Soc., 138, 2047–2065, 2012. a, b
Ebert, E. E., Janowiak, J. E., and Kidd, C.: Comparison of near-real-time precipitation estimates from satellite observations and numerical models, B. Am. Meteorol. Soc., 88, 47–64, 2007. a
Eriksson, P., Ekelund, R., Mendrok, J., Brath, M., Lemke, O., and Buehler, S. A.: A general database of hydrometeor single scattering properties at microwave and sub-millimetre wavelengths, Earth Syst. Sci. Data, 10, 1301–1326, https://doi.org/10.5194/essd-10-1301-2018, 2018. a
Fabry, F. and Sun, J.: For how long should what data be assimilated for the mesoscale forecasting of convection and why? Part I: On the propagation of initial condition errors and their implications for data assimilation, Mon. Weather Rev., 138, 242–255, 2010. a
Galligani, V., Prigent, C., Defer, E., Jimenez, C., and Eriksson, P.: The impact of the melting layer on the passive microwave cloud scattering signal observed from satellites: A study using TRMM microwave passive and active measurements, J. Geophys. Res.-Atmos., 118, 5667–5678, 2013. a
Maxwell Garnett, J. M.: XII. Colours in metal glasses and in metallic films, Philos. T. R. Soc. A, Containing Papers of a Mathematical or Physical Character, 203, 385–420, 1904. a
Geer, A. J.: Physical characteristics of frozen hydrometeors inferred with parameter estimation, Atmos. Meas. Tech., 14, 5369–5395, https://doi.org/10.5194/amt-14-5369-2021, 2021. a
Geer, A. J. and Baordo, F.: Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies, Atmos. Meas. Tech., 7, 1839–1860, https://doi.org/10.5194/amt-7-1839-2014, 2014. a, b
Geer, A. J., Bauer, P., Lonitz, K., Barlakas, V., Eriksson, P., Mendrok, J., Doherty, A., Hocking, J., and Chambon, P.: Bulk hydrometeor optical properties for microwave and sub-millimetre radiative transfer in RTTOV-SCATT v13.0, Geosci. Model Dev., 14, 7497–7526, https://doi.org/10.5194/gmd-14-7497-2021, 2021. a, b, c, d, e, f, g
Giangrande, S. E., Krause, J. M., and Ryzhkov, A. V.: Automatic designation of the melting layer with a polarimetric prototype of the WSR-88D radar, J. Appl. Meteorol. Climatol., 47, 1354–1364, 2008. a
Hamada, A. and Takayabu, Y. N.: Improvements in detection of light precipitation with the Global Precipitation Measurement dual-frequency precipitation radar (GPM DPR), J. Atmos. Ocean. Technol., 33, 653–667, 2016. a
Hashino, T., Satoh, M., Hagihara, Y., Kubota, T., Matsui, T., Nasuno, T., and Okamoto, H.: Evaluating cloud microphysics from NICAM against CloudSat and CALIPSO, J. Geophys. Res.-Atmos., 118, 7273–7292, 2013. a
Haynes, J. M., Marchand, R. T., Luo, Z., Bodas-Salcedo, A., and Stephens, G. L.: A multipurpose radar simulation package: QuickBeam, B. Am. Meteorol. Soc., 88, 1723–1728, 2007. a
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E. J., Sorooshian, S., Tan, J., and Xie, P.: NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG), Algorithm Theoretical Basis Document (ATBD) Version, 4, 2018. a
Iguchi, T., Seto, S., Meneghini, R., Yoshida, N., Awaka, J., Le, M., Chandrasekar, V., and Kubota, T.: GPM/DPR level-2 algorithm theoretical basis document, NASA Goddard Space Flight Center, https://www.eorc.jaxa.jp/GPM/doc/algorithm/ATBD_DPR_201811_with_Appendix3b.pdf (last access: January 2023), 2010. a, b, c, d, e, f
Iguchi, T., Matsui, T., Tao, W.-K., Khain, A. P., Phillips, V. T., Kidd, C., L’Ecuyer, T., Braun, S. A., and Hou, A.: WRF–SBM simulations of melting-layer structure in mixed-phase precipitation events observed during LPVEx, J. Appl. Meteorol. Climatol., 53, 2710–2731, 2014. a
Ikuta, Y., Okamoto, K., and Kubota, T.: One-dimensional maximum-likelihood estimation for spaceborne precipitation radar data assimilation, Quarterly J. Roy. Meteorol. Soc., 147, 858–875, 2021a. a
Ikuta, Y., Satoh, M., Sawada, M., Kusabiraki, H., and Kubota, T.: Improvement of the Cloud Microphysics Scheme of the Mesoscale Model at the Japan Meteorological Agency Using Spaceborne Radar and Microwave Imager of the Global Precipitation Measurement as Reference, Mon. Weather Rev., 149, 3803–3819, 2021b. a
Johnson, B. T., Olson, W. S., and Skofronick-Jackson, G.: The microwave properties of simulated melting precipitation particles: sensitivity to initial melting, Atmos. Meas. Tech., 9, 9–21, https://doi.org/10.5194/amt-9-9-2016, 2016. a, b, c, d
Johnson, B. T. and Boukabara, S. A.: Development of the Community Active Sensor Module (CASM): Forward Simulation, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2016-154, in review, 2016. a
Johnson, B. T., Dang, C., Stegmann, P., Liu, Q., Moradi, I., and Auligne, T.: The Community Radiative Transfer Model (CRTM): Community-focused collaborative model development accelerating research to operations, B. Am. Meteorol. Soc., 104, E1817–E1830, 2023. a
Karrer, M., Dias Neto, J., von Terzi, L., and Kneifel, S.: Melting Behavior of Rimed and Unrimed Snowflakes Investigated With Statistics of Triple-Frequency Doppler Radar Observations, J. Geophys. Res.-Atmos., 127, e2021JD035907, https://doi.org/10.1029/2021JD035907, 2022. a
Kollias, P. and Albrecht, B.: Why the melting layer radar reflectivity is not bright at 94 GHz, Geophys. Res. Lett., 32, L24818, https://doi.org/10.1029/2005GL024074, 2005. a, b, c
Kotsuki, S., Terasaki, K., and Miyoshi, T.: GPM/DPR precipitation compared with a 3.5-km-resolution NICAM simulation, Sola, 10, 204–209, 2014. a
Kotsuki, S., Terasaki, K., Satoh, M., and Miyoshi, T.: Ensemble-Based Data Assimilation of GPM DPR Reflectivity: Cloud Microphysics Parameter Estimation With the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), J. Geophys. Res.-Atmos., 128, e2022JD037447, https://doi.org/10.1029/2022JD037447, 2023. a
Le, M. and Chandrasekar, V.: Precipitation type classification method for dual-frequency precipitation radar (DPR) onboard the GPM, IEEE T. Geosci. Remote Sens., 51, 1784–1790, 2012. a
Le, M. and Chandrasekar, V.: Graupel and hail identification algorithm for the dual-frequency precipitation radar (DPR) on the GPM core satellite, J. Meteorol. Soc. JPN II, 99, 49–65, 2021. a
Le, M., Chandrasekar, V., and Biswas, S.: An algorithm to identify surface snowfall from GPM DPR observations, IEEE T. Geosci. Remote Sens., 55, 4059–4071, 2017. a
Levene, H.: Contributions to probability and statistics, Essays in honor of Harold Hotelling, 278, 292, 1960. a
Lhermitte, R. M.: Observation of rain at vertical incidence with a 94 GHz Doppler radar: An insight on Mie scattering, Geophys. Res. Lett., 15, 1125–1128, 1988. a
Li, Y., Zipser, E. J., Krueger, S. K., and Zulauf, M. A.: Cloud-resolving modeling of deep convection during KWAJEX. Part I: Comparison to TRMM satellite and ground-based radar observations, Mon. Weather Rev., 136, 2699–2712, 2008. a
Liao, L., Meneghini, R., Nowell, H. K., and Liu, G.: Scattering computations of snow aggregates from simple geometrical particle models, IEEE J. Select. Top. Appl. Earth Observ. Remote Sens., 6, 1409–1417, 2013. a
Liu, C. and Zipser, E. J.: The global distribution of largest, deepest, and most intense precipitation systems, Geophys. Res. Lett., 42, 3591–3595, 2015. a
Liu, G.: A database of microwave single-scattering properties for nonspherical ice particles, B. Am. Meteorol. Soc., 89, 1563–1570, 2008. a
Liu, N. and Liu, C.: Global distribution of deep convection reaching tropopause in 1 year GPM observations, J. Geophys. Res.-Atmos., 121, 3824–3842, 2016. a
Lopez, P.: Implementation and validation of a new prognostic large-scale cloud and precipitation scheme for climate and data-assimilation purposes, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, Appl. Meteorol. Phys. Oceanogr., 128, 229–257, 2002. a
Mai, L., Yang, S., Wang, Y., and Li, R.: Impacts of Shape Assumptions on Z–R Relationship and Satellite Remote Sensing Clouds Based on Model Simulations and GPM Observations, Remote Sens., 15, 1556, https://doi.org/10.3390/rs15061556, 2023. a
Marchand, R., Haynes, J., Mace, G. G., Ackerman, T., and Stephens, G.: A comparison of simulated cloud radar output from the multiscale modeling framework global climate model with CloudSat cloud radar observations, J. Geophys. Res.-Atmos., 114, D00A20, https://doi.org/10.1029/2008JD009790, 2009. a
Marshall, J. S. and Palmer, W. M.: The distribution of raindrops with size, J. Meteorol., 5, 165–166, 1948. a
Matsui, T., Iguchi, T., Li, X., Han, M., Tao, W.-K., Petersen, W., L'Ecuyer, T., Meneghini, R., Olson, W., Kummerow, C. D., Hou, A. Y., Schwaller, M. R., Stocker, E. F., and Kwiatkowski, J.: GPM satellite simulator over ground validation sites, B. Am. Meteorol. Soc., 94, 1653–1660, 2013. a
Mätzler, C.: Thermal microwave radiation: applications for remote sensing, Iet, 52, 584 pp., https://doi.org/10.1049/PBEW052E, 2006. a, b
Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020. a
Meneghini, R. and Liao, L.: Effective dielectric constants of mixed-phase hydrometeors, J. Atmos. Ocean. Technol., 17, 628–640, 2000. a
Mitra, S., Vohl, O., Ahr, M., and Pruppacher, H.: A wind tunnel and theoretical study of the melting behavior of atmospheric ice particles. IV: Experiment and theory for snow flakes, J. Atmos. Sci., 47, 584–591, 1990. a
Moradi, I., Johnson, B., Stegmann, P., Holdaway, D., Heymsfield, G., Gelaro, R., and McCarty, W.: Developing a Radar Signal Simulator for the Community Radiative Transfer Model, IEEE T. Geosci. Remote Sens., 61, https://doi.org/10.1109/TGRS.2023.3330067, 2023. a, b
Olson, W., Bauer, P., Kummerow, C. D., Hong, Y., and Tao, W.-K.: A melting-layer model for passive/active microwave remote sensing applications. Part II: Simulation of TRMM observations, J. Appl. Meteorol. Climatol., 40, 1164–1179, 2001. a
Park, S.: IFS Doc-Physical Processes; IFS Documentation CY45R1, no. 1, https://doi.org/10.21957/4whwo8jw0, 2018. a
Petty, G. W. and Huang, W.: Microwave backscatter and extinction by soft ice spheres and complex snow aggregates, J. Atmos. Sci., 67, 769–787, 2010. a
Ringer, M., Edwards, J., and Slingo, A.: Simulation of satellite channel radiances in the Met Office Unified Model, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, Appl. Meteorol. Phys. Oceanogr., 129, 1169–1190, 2003. a
Roberts, N. M. and Lean, H. W.: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events, Mon. Weather Rev., 136, 78–97, 2008. a
Romatschke, U.: Melting layer detection and observation with the NCAR airborne W-band radar, Remote Sens., 13, 1660, https://doi.org/10.21957/4whwo8jw0, 2021. a
RTTOV: RTTOV version 13, EUMETSAT Satellite Application Facility on Numerical Weather Prediction (NWP SAF), RTTOV [code], https://nwp-saf.eumetsat.int/site/software/rttov/rttov-v13/ (last access: January, 2023), 2025. a
Sassen, K., Campbell, J. R., Zhu, J., Kollias, P., Shupe, M., and Williams, C.: Lidar and triple-wavelength Doppler radar measurements of the melting layer: A revised model for dark-and brightband phenomena, J. Appl. Meteorol., 44, 301–312, 2005. a
Sassen, K., Matrosov, S., and Campbell, J.: CloudSat spaceborne 94 GHz radar bright bands in the melting layer: An attenuation-driven upside-down lidar analog, Geophys. Res. Lett., 34, L16818, https://doi.org/10.1029/2007GL030291, 2007. a
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018. a
Skofronick-Jackson, G., Petersen, W. A., Berg, W., Kidd, C., Stocker, E. F., Kirschbaum, D. B., Kakar, R., Braun, S. A., Huffman, G. J., Iguchi, T., Kirstetter, P. E., Kummerow, C., Meneghini, R., Oki, R., Olson, W. S., Takayabu, Y. N., Furukawa, K., and Wilheit, T.: The Global Precipitation Measurement (GPM) mission for science and society, B. Am. Meteorol. Soc., 98, 1679–1695, 2017. a
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and the CloudSat Science Team: The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation, B. Am. Meteorol. Soc., 83, 1771–1790, 2002. a
Sun, Y., Dong, X., Cui, W., Zhou, Z., Fu, Z., Zhou, L., Deng, Y., and Cui, C.: Vertical structures of typical Meiyu precipitation events retrieved from GPM-DPR, J. Geophys. Res.-Atmos., 125, e2019JD031466, https://doi.org/10.1029/2019JD031466, 2020. a, b
Szyrmer, W. and Zawadzki, I.: Modeling of the melting layer. Part I: Dynamics and microphysics, J. Atmos. Sci., 56, 3573–3592, 1999. a
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in large-scale models, Mon. Weather Rev., 117, 1779–1800, 1989. a
Zhang, A. and Fu, Y.: Life cycle effects on the vertical structure of precipitation in East China measured by Himawari-8 and GPM DPR, Mon. Weather Rev., 146, 2183–2199, 2018. a
Zhang, J., Langston, C., and Howard, K.: Brightband identification based on vertical profiles of reflectivity from the WSR-88D, J. Atmos. Ocean. Technol., 25, 1859–1872, 2008. a
Zimmerman, D. W. and Zumbo, B. D.: Rank transformations and the power of the Student t test and Welch t'test for non-normal populations with unequal variances, Can. J. Exp. Psychol., 47, 523, https://doi.org/10.1037/h0078850, 1993. a
Short summary
This study provides a detailed description of the radar simulator available within version 13 of the RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) software. It is applied to the Météo-France global numerical weather prediction model, with the objective of simulating Dual-frequency Precipitation Radar reflectivity observations. Additionally, the simulation of the bright band is addressed and then successfully applied to model forecasts for the purpose of classifying NWP (numerical weather prediction) model columns between stratiform and convective categories.
This study provides a detailed description of the radar simulator available within version 13 of...
