Articles | Volume 9, issue 11
https://doi.org/10.5194/amt-9-5441-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-9-5441-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
14 Nov 2016
Research article |
| 14 Nov 2016
The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars
Institute of Atmospheric Sciences and Climate (ISAC), National
Research Council of Italy (CNR), 00133 Rome, Italy
Giulia Panegrossi
Institute of Atmospheric Sciences and Climate (ISAC), National
Research Council of Italy (CNR), 00133 Rome, Italy
Daniele Casella
Institute of Atmospheric Sciences and Climate (ISAC), National
Research Council of Italy (CNR), 00133 Rome, Italy
Anna C. Marra
Institute of Atmospheric Sciences and Climate (ISAC), National
Research Council of Italy (CNR), 00133 Rome, Italy
Francesco Di Paola
Institute of Methodologies for Environmental Analysis (IMAA), Italian
National Research Council of Italy (CNR), C.da S.Loja, Tito Scalo, 85050
Potenza, Italy
Stefano Dietrich
Institute of Atmospheric Sciences and Climate (ISAC), National
Research Council of Italy (CNR), 00133 Rome, Italy
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Results show the important and positive impact of using lightning data to improve the precipitation forecast. The time range, however, is very important because the performance decreases steadily and substantially with forecasting time.
Stefano Federico, Rosa Claudia Torcasio, Paolo Sanò, Daniele Casella, Monica Campanelli, Jan Fokke Meirink, Ping Wang, Stefania Vergari, Henri Diémoz, and Stefano Dietrich
Atmos. Meas. Tech., 10, 2337–2352, https://doi.org/10.5194/amt-10-2337-2017, https://doi.org/10.5194/amt-10-2337-2017, 2017
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Stefano Federico, Marco Petracca, Giulia Panegrossi, and Stefano Dietrich
Nat. Hazards Earth Syst. Sci., 17, 61–76, https://doi.org/10.5194/nhess-17-61-2017, https://doi.org/10.5194/nhess-17-61-2017, 2017
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The motivation of this study is to use lightning observations to improve the precipitation forecast at the short range (3 h). For this purpose 20 case studies, occurring in fall 2012, were analyzed using a meteorological model, whose set-up is applicable in real-time weather forecasting. Lightning observations were provided by the LINET network. Results show a systematic improvement of the 3 h precipitation forecast when lightning observations are used.
Martina Buiat, Federico Porcù, and Stefano Dietrich
Atmos. Meas. Tech., 10, 221–230, https://doi.org/10.5194/amt-10-221-2017, https://doi.org/10.5194/amt-10-221-2017, 2017
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Short summary
The cloud radar on board the NASA CloudSat mission provides information on the vertical structure of the cloud that, in the present study, is matched to ground-based measurements of lightning occurrences. The aim of this research was to study the relationship between the ice content of the cloud and its capability to produce lightning. Results show the importance of high ice content, especially close to the cloud top, for producing lightning.
N. Roberto, E. Adirosi, L. Baldini, D. Casella, S. Dietrich, P. Gatlin, G. Panegrossi, M. Petracca, P. Sanò, and A. Tokay
Atmos. Meas. Tech., 9, 535–552, https://doi.org/10.5194/amt-9-535-2016, https://doi.org/10.5194/amt-9-535-2016, 2016
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This study examines various microphysical properties of liquid and solid hydrometeors to investigate their relationship with lightning activity. Measurements were collected from the Polar 55C dual-polarization radar, a 2-DVD, and LINET. From the analysis of three significant case studies, linear relations between the total mass of graupel and the number of strokes were found. Results point out the key role of ice mass in determining the electrical charging of convective clouds.
D. Casella, G. Panegrossi, P. Sanò, L. Milani, M. Petracca, and S. Dietrich
Atmos. Meas. Tech., 8, 1217–1232, https://doi.org/10.5194/amt-8-1217-2015, https://doi.org/10.5194/amt-8-1217-2015, 2015
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P. Sanò, G. Panegrossi, D. Casella, F. Di Paola, L. Milani, A. Mugnai, M. Petracca, and S. Dietrich
Atmos. Meas. Tech., 8, 837–857, https://doi.org/10.5194/amt-8-837-2015, https://doi.org/10.5194/amt-8-837-2015, 2015
L. Milani, F. Porcù, D. Casella, S. Dietrich, G. Panegrossi, M. Petracca, and P. Sanò
The Cryosphere Discuss., https://doi.org/10.5194/tcd-9-141-2015, https://doi.org/10.5194/tcd-9-141-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
The aim of this work is to show that the CloudSat Cloud Profiling Radar (CPR) can be a valuable source of snowfall rate data in Antarctica that can be used at different temporal scales. Two years of CloudSat data over Antarctica are analyzed and two different approaches for precipitation estimates are considered. The results show that CPR can provide valuable support to the sparse network of ground-based instruments both for numerical model validation and climatological studies.
S. Federico, E. Avolio, M. Petracca, G. Panegrossi, P. Sanò, D. Casella, and S. Dietrich
Nat. Hazards Earth Syst. Sci., 14, 2933–2950, https://doi.org/10.5194/nhess-14-2933-2014, https://doi.org/10.5194/nhess-14-2933-2014, 2014
Short summary
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This paper shows the implementation of a simple model for simulating lightning into the RAMS model.
The methodology is applied to six case studies that occurred in central Italy and the results are verified against LINET observations.
Advantages and weaknesses of the methodology are discussed.
E. Ricciardelli, D. Cimini, F. Di Paola, F. Romano, and M. Viggiano
Hydrol. Earth Syst. Sci., 18, 2559–2576, https://doi.org/10.5194/hess-18-2559-2014, https://doi.org/10.5194/hess-18-2559-2014, 2014
R. Ferretti, E. Pichelli, S. Gentile, I. Maiello, D. Cimini, S. Davolio, M. M. Miglietta, G. Panegrossi, L. Baldini, F. Pasi, F. S. Marzano, A. Zinzi, S. Mariani, M. Casaioli, G. Bartolini, N. Loglisci, A. Montani, C. Marsigli, A. Manzato, A. Pucillo, M. E. Ferrario, V. Colaiuda, and R. Rotunno
Hydrol. Earth Syst. Sci., 18, 1953–1977, https://doi.org/10.5194/hess-18-1953-2014, https://doi.org/10.5194/hess-18-1953-2014, 2014
D. Cimini, F. Romano, E. Ricciardelli, F. Di Paola, M. Viggiano, F. S. Marzano, V. Colaiuda, E. Picciotti, G. Vulpiani, and V. Cuomo
Atmos. Meas. Tech., 6, 3181–3196, https://doi.org/10.5194/amt-6-3181-2013, https://doi.org/10.5194/amt-6-3181-2013, 2013
A. Mugnai, D. Casella, E. Cattani, S. Dietrich, S. Laviola, V. Levizzani, G. Panegrossi, M. Petracca, P. Sanò, F. Di Paola, D. Biron, L. De Leonibus, D. Melfi, P. Rosci, A. Vocino, F. Zauli, P. Pagliara, S. Puca, A. Rinollo, L. Milani, F. Porcù, and F. Gattari
Nat. Hazards Earth Syst. Sci., 13, 1959–1981, https://doi.org/10.5194/nhess-13-1959-2013, https://doi.org/10.5194/nhess-13-1959-2013, 2013
E. A. Smith, H. W.-Y. Leung, J. B. Elsner, A. V. Mehta, G. J. Tripoli, D. Casella, S. Dietrich, A. Mugnai, G. Panegrossi, and P. Sanò
Nat. Hazards Earth Syst. Sci., 13, 1185–1208, https://doi.org/10.5194/nhess-13-1185-2013, https://doi.org/10.5194/nhess-13-1185-2013, 2013
M. Formenton, G. Panegrossi, D. Casella, S. Dietrich, A. Mugnai, P. Sanò, F. Di Paola, H.-D. Betz, C. Price, and Y. Yair
Nat. Hazards Earth Syst. Sci., 13, 1085–1104, https://doi.org/10.5194/nhess-13-1085-2013, https://doi.org/10.5194/nhess-13-1085-2013, 2013
A. Mugnai, E. A. Smith, G. J. Tripoli, B. Bizzarri, D. Casella, S. Dietrich, F. Di Paola, G. Panegrossi, and P. Sanò
Nat. Hazards Earth Syst. Sci., 13, 887–912, https://doi.org/10.5194/nhess-13-887-2013, https://doi.org/10.5194/nhess-13-887-2013, 2013
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A new non-linearity correction method for the spectrum from the Geostationary Inferometric Infrared Sounder on board Fengyun-4 satellites and its preliminary assessments
Observing atmospheric rivers using multi-GNSS airborne radio occultation: system description and data evaluation
Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data
Unfiltering of the EarthCARE Broadband Radiometer (BBR) observations: the BM-RAD product
Variance estimations in the presence of intermittent interference and their applications to incoherent scatter radar signal processing
A clustering-based method for identifying and tracking squall lines
A multi-instrument fuzzy logic boundary-layer-top detection algorithm
Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models
Scale separation for gravity wave analysis from 3D temperature observations in the mesosphere and lower thermosphere (MLT) region
Estimating the refractivity bias of FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) radio occultation in the deep troposphere
Observed impact of the GNSS clock data rate on Radio Occultation bending angles for Sentinel-6A and COSMIC-2
High Spectral Resolution Lidar – generation 2 (HSRL-2) retrievals of ocean surface wind speed: methodology and evaluation
Erlend Øydvin, Renaud Gaban, Jafet Andersson, Remco (C. Z.) van de Beek, Mareile Astrid Wolff, Nils-Otto Kitterød, Christian Chwala, and Vegard Nilsen
Atmos. Meas. Tech., 18, 2279–2293, https://doi.org/10.5194/amt-18-2279-2025, https://doi.org/10.5194/amt-18-2279-2025, 2025
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We present a novel method for classifying rain and snow by combining data from commercial microwave links (CMLs) with weather radar. We compare this to a reference method using dew point temperature for precipitation type classification. Evaluations with nearby disdrometers show that CMLs improve the classification of dry snow and rainfall, outperforming the reference method.
Juliëtte C. S. Anema, K. Folkert Boersma, Lieuwe G. Tilstra, Olaf N. E. Tuinder, and Willem W. Verstraeten
Atmos. Meas. Tech., 18, 1961–1979, https://doi.org/10.5194/amt-18-1961-2025, https://doi.org/10.5194/amt-18-1961-2025, 2025
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Long-term records of plant fluorescence offer vital insights into changing vegetation activity. The GOME-2A sensor provides extensive global observations but suffers from calibration and instrument degradation, which affects data consistency. This study presents the SIFTER v3 algorithm, which effectively resolves these issues and includes other improvements, resulting in robust, accurate, and consistent GOME-2A fluorescence measurements from 2007 to 2017.
Nadia Smith and Christopher D. Barnet
Atmos. Meas. Tech., 18, 1823–1839, https://doi.org/10.5194/amt-18-1823-2025, https://doi.org/10.5194/amt-18-1823-2025, 2025
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CLIMCAPS extends the Aqua AIRS+AMSU record with retrievals from CrIS+ATMS on Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System series (JPSS-1 to JPSS-4). With “continuous”, we mean a data record that is consistent in its characterization of natural variation despite changes in source instrumentation. Here we investigate how sounding continuity can improve across the full CLIMCAPS record (2002 to the present day), spanning multiple instruments and satellites.
Tianwen Wei, Mengya Wang, Kenan Wu, Jinlong Yuan, Haiyun Xia, and Simone Lolli
Atmos. Meas. Tech., 18, 1841–1857, https://doi.org/10.5194/amt-18-1841-2025, https://doi.org/10.5194/amt-18-1841-2025, 2025
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This study analyzes three years of wind lidar measurements to explore the dynamics of the urban planetary boundary layer in Hefei, China. Results reveal that nocturnal low-level jets are most frequent in spring and intensify in summer, significantly enhancing turbulence and shear near the surface, particularly at night. Additionally, cloud cover raises the mixing layer height by approximately 100 m at night due to the greenhouse effect but reduces it by up to 200 m in the afternoon.
Matthieu Vernay, Matthieu Lafaysse, and Clotilde Augros
Atmos. Meas. Tech., 18, 1731–1755, https://doi.org/10.5194/amt-18-1731-2025, https://doi.org/10.5194/amt-18-1731-2025, 2025
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This paper provides a comprehensive evaluation of the quality of radar-based precipitation estimation in mountainous areas and presents a method to mitigate the main shortcomings identified. It then compares three different ensemble analysis methods that combine radar-based precipitation estimates with forecasts from an ensemble numerical weather prediction model.
Sabine Wüst, Lisa Küchelbacher, Franziska Trinkl, and Michael Bittner
Atmos. Meas. Tech., 18, 1591–1607, https://doi.org/10.5194/amt-18-1591-2025, https://doi.org/10.5194/amt-18-1591-2025, 2025
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Information on the energy transported by atmospheric gravity waves (GWs) is crucial for improving atmosphere models. Most space-based studies report the potential energy. We use Aeolus wind data to estimate the kinetic energy (density). However, the data quality is a challenge for such analyses, as the accuracy of the data is in the range of typical GW amplitudes. We find a temporal coincidence between enhanced or breaking planetary waves and enhanced gravity wave kinetic energy density.
Natalie E. Theeuwes, Janet F. Barlow, Antti Mannisenaho, Denise Hertwig, Ewan O'Connor, and Alan Robins
Atmos. Meas. Tech., 18, 1355–1371, https://doi.org/10.5194/amt-18-1355-2025, https://doi.org/10.5194/amt-18-1355-2025, 2025
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A Doppler lidar was placed in a highly built-up area in London to measure wakes from tall buildings during a period of 1 year. We were able to detect wakes and assess their dependence on wind speed, wind direction, and atmospheric stability.
Chengli Ji, Qiankai Jin, Feilong Li, Yuyang Liu, Zhicheng Wang, Jiajia Mao, Xiaoyu Ren, Yan Xiang, Wanlin Jian, Peitao Zhao, and Zhenyi Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1171, https://doi.org/10.5194/egusphere-2025-1171, 2025
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This study presents the humidity measurements with a synergetic algorithm combining Raman lidar, MVR, and satellite. The results from 47 sites in China show the best correlation over 0.9 concerning the radiosonde measurements. It validates the RH accuracy with various data integrations. Three representative sites present the different seasonal characteristics indicating the geographic and height influences on the RH vertical distribution.
Maurice Roots, John T. Sullivan, and Belay Demoz
Atmos. Meas. Tech., 18, 1269–1282, https://doi.org/10.5194/amt-18-1269-2025, https://doi.org/10.5194/amt-18-1269-2025, 2025
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This paper presents a supervised-machine-learning approach for the automatic isolation of nocturnal low-level jets (NLLJs) using observations from a radar wind profiler. This analysis isolated 90 southwesterly NLLJs observed from May to September 2017–2021, highlighting key features in the evolution and morphology of the mid-Atlantic NLLJ.
Padmanabhan Thiruvengadam, Guillaume Lesage, Ambinintsoa Volatiana Ramanamahefa, and Joël Van Baelen
Atmos. Meas. Tech., 18, 1185–1191, https://doi.org/10.5194/amt-18-1185-2025, https://doi.org/10.5194/amt-18-1185-2025, 2025
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This study explores how the joints in a weather radar's protective cover affect its measurements. We developed a new method to correct these errors, improving the accuracy of the radar's data. Our method was tested during an intense cyclone on Réunion Island, demonstrating significant improvements in data accuracy. This research is crucial for enhancing weather predictions and understanding, particularly in challenging terrains.
Dong L. Wu, Valery A. Yudin, Kyu-Myong Kim, Mohar Chattopadhyay, Lawrence Coy, Ruth S. Lieberman, C. C. Jude H. Salinas, Jae N. Lee, Jie Gong, and Guiping Liu
Atmos. Meas. Tech., 18, 843–863, https://doi.org/10.5194/amt-18-843-2025, https://doi.org/10.5194/amt-18-843-2025, 2025
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Global Navigation Satellite System radio occultation data help monitor climate and weather prediction but are affected by residual ionospheric errors (RIEs). A new excess-phase-gradient method detects and corrects RIEs, showing both positive and negative values, varying by latitude, time, and solar activity. Tests show that RIE impacts polar stratosphere temperatures in models, with differences up to 3–4 K. This highlights the need for RIE correction to improve the accuracy of data assimilation.
Damao Zhang, Jennifer Comstock, Chitra Sivaraman, Kefei Mo, Raghavendra Krishnamurthy, Jingjing Tian, Tianning Su, Zhanqing Li, and Natalia Roldán-Henao
EGUsphere, https://doi.org/10.5194/egusphere-2024-3959, https://doi.org/10.5194/egusphere-2024-3959, 2025
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Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Atmos. Meas. Tech., 18, 793–816, https://doi.org/10.5194/amt-18-793-2025, https://doi.org/10.5194/amt-18-793-2025, 2025
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Accurate KDP estimates are crucial in radar-based applications. We quantify the uncertainties of several publicly available KDP estimation methods for multiple rainfall intensities. We use C-band weather radar observations and employed a self-consistency KDP, estimated from reflectivity and differential reflectivity, as a framework for the examination. Our study provides guidance for the performance, uncertainties, and optimisation of the methods, focusing mainly on accuracy and robustness.
Lei Liu, Natalia Bliankinshtein, Yi Huang, John R. Gyakum, Philip M. Gabriel, Shiqi Xu, and Mengistu Wolde
Atmos. Meas. Tech., 18, 471–485, https://doi.org/10.5194/amt-18-471-2025, https://doi.org/10.5194/amt-18-471-2025, 2025
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This study evaluates and compares a new microwave hyperspectrometer with an infrared hyperspectrometer for clear-sky temperature and water vapor retrievals. The analysis reveals that the information content of the infrared hyperspectrometer exceeds that of the microwave hyperspectrometer and provides higher vertical resolution in ground-based zenith measurements. Leveraging the ground–airborne synergy between the two instruments yielded optimal sounding results.
David D. Turner, Maria P. Cadeddu, Julia Simonson, and Timothy J. Wagner
EGUsphere, https://doi.org/10.5194/egusphere-2024-4124, https://doi.org/10.5194/egusphere-2024-4124, 2025
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When deriving a geophysical variable from remote sensors, the uncertainty and information content are critical. The latter quantify specifies what fraction of a real perturbation would be observed in the derived variable. This paper outlines, for the first time, a methodology for propagating the information content from multiple remote sensors into a derived product, using horizontal advection as an example.
Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Alain Geiger, Laura Crocetti, Gregor Moeller, Benedikt Soja, and Markus Rothacher
Atmos. Meas. Tech., 18, 57–72, https://doi.org/10.5194/amt-18-57-2025, https://doi.org/10.5194/amt-18-57-2025, 2025
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This work investigates whether machine learning (ML) can offer an alternative to existing methods to map radio occultation (RO) products, allowing the extraction of information not visible in direct observations. ML can further improve the results of Bayesian interpolation, a state-of-the-art method to map RO observations. The results display improvements in horizontal and temporal domains, at heights ranging from the planetary boundary layer up to the lower stratosphere, and for all seasons.
Luuk D. van der Valk, Oscar K. Hartogensis, Miriam Coenders-Gerrits, Rolf W. Hut, Bas Walraven, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2024-2974, https://doi.org/10.5194/egusphere-2024-2974, 2025
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Commercial microwave links (CMLs), part of mobile phone networks, transmit comparable signals as instruments specially designed to estimate evaporation. Therefore, we investigate if CMLs could be used to estimate evaporation, even though they have not been designed for this purpose. Our results illustrate the potential of using CMLs to estimate evaporation, especially given their global coverage, but also outline some major drawbacks, often a consequence of unfavourable design choices for CMLs.
Andreas Foth, Moritz Lochmann, Pablo Saavedra Garfias, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 7169–7181, https://doi.org/10.5194/amt-17-7169-2024, https://doi.org/10.5194/amt-17-7169-2024, 2024
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Microwave radiometers are usually not able to provide atmospheric quantities such as temperature profiles during rain. We present a method based on a selection of specific frequencies and elevation angles from microwave radiometer observations. A comparison with a numerical weather prediction model shows the presented method allows low-level temperature profiles during rain to be resolved, with rain rates of up to 2.5 mm h−1,, which was not possible before with state-of-the-art retrievals.
Lev D. Labzovskii, Gerd-Jan van Zadelhoff, David P. Donovan, Jos de Kloe, L. Gijsbert Tilstra, Ad Stoffelen, Damien Josset, and Piet Stammes
Atmos. Meas. Tech., 17, 7183–7208, https://doi.org/10.5194/amt-17-7183-2024, https://doi.org/10.5194/amt-17-7183-2024, 2024
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The Atmospheric Laser Doppler Instrument (ALADIN) on the Aeolus satellite was the first of its kind to measure high-resolution vertical profiles of aerosols and cloud properties from space. We present an algorithm that produces Aeolus lidar surface returns (LSRs), containing useful information for measuring UV reflectivity. Aeolus LSRs matched well with existing UV reflectivity data from other satellites, like GOME-2 and TROPOMI, and demonstrated excellent sensitivity to modeled snow cover.
Thomas Hocking, Thorsten Mauritsen, and Linda Megner
Atmos. Meas. Tech., 17, 7077–7095, https://doi.org/10.5194/amt-17-7077-2024, https://doi.org/10.5194/amt-17-7077-2024, 2024
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The imbalance between the energy the Earth absorbs from the Sun and the energy the Earth emits back into space gives rise to climate change, but measuring the small imbalance is challenging. We simulate satellites in various orbits to investigate how well they sample the imbalance and find that the best option is to combine at least two satellites that see complementary parts of the Earth and cover the daily and annual cycles. This information is useful when planning future satellite missions.
Almudena Velázquez Blázquez, Carlos Domenech, Edward Baudrez, Nicolas Clerbaux, Carla Salas Molar, and Nils Madenach
Atmos. Meas. Tech., 17, 7007–7026, https://doi.org/10.5194/amt-17-7007-2024, https://doi.org/10.5194/amt-17-7007-2024, 2024
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This paper focuses on the BMA-FLX processor, in which thermal and solar top-of-atmosphere radiative fluxes are obtained from longwave and shortwave radiances measured along track by the EarthCARE Broadband Radiometer (BBR). The BBR measurements, at three fixed viewing angles (fore, nadir, aft), are co-registered either at the surface or at a reference level. A combined flux from the three BRR views is obtained. The algorithm has been successfully validated against test scenes.
Wolf Knöller, Gholamhossein Bagheri, Philipp von Olshausen, and Michael Wilczek
Atmos. Meas. Tech., 17, 6913–6931, https://doi.org/10.5194/amt-17-6913-2024, https://doi.org/10.5194/amt-17-6913-2024, 2024
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Three-dimensional (3D) wind velocity measurements are of major importance for the characterization of atmospheric turbulence. This paper presents a detailed study of the measurement uncertainty of a three-beam wind lidar designed for mounting on airborne platforms. Considering the geometrical constraints, the analysis provides quantitative estimates for the measurement uncertainty of all components of the 3D wind vector. As a result, we propose optimized post-processing for error reduction.
Bianca Adler, David D. Turner, Laura Bianco, Irina V. Djalalova, Timothy Myers, and James M. Wilczak
Atmos. Meas. Tech., 17, 6603–6624, https://doi.org/10.5194/amt-17-6603-2024, https://doi.org/10.5194/amt-17-6603-2024, 2024
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Continuous profile observations of temperature and humidity in the lowest part of the atmosphere are essential for the evaluation of numerical weather prediction models and data assimilation for better weather forecasts. Such profiles can be retrieved from passive ground-based remote sensing instruments like infrared spectrometers and microwave radiometers. In this study, we describe three recent modifications to the retrieval framework TROPoe for improved temperature and humidity profiles.
Yunfan Yang, Wei Han, Haofei Sun, Jun Li, Jiapeng Yan, and Zhiqiu Gao
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-175, https://doi.org/10.5194/amt-2024-175, 2024
Revised manuscript accepted for AMT
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Our research improves satellite-based precipitation monitoring by using deep learning to reconstruct radar observations from passive microwave radiances. Active radar is costly, so we focus on a more accessible approach. Using data from the FengYun-3G satellite, we successfully tracked severe weather like Typhoon Khanun and heavy rainfall in Beijing in 2023. This method enhances global precipitation data and helps better understand extreme weather.
Bernat Puigdomènech Treserras and Pavlos Kollias
Atmos. Meas. Tech., 17, 6301–6314, https://doi.org/10.5194/amt-17-6301-2024, https://doi.org/10.5194/amt-17-6301-2024, 2024
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The paper presents a comprehensive approach to improve the geolocation accuracy of spaceborne radar and lidar systems, crucial for the successful interpretation of data from the upcoming EarthCARE mission. The paper details the technical background of the presented methods and various examples of geolocation analyses, including a short period of CloudSat observations when the star tracker was not operating properly and lifetime statistics from the CloudSat and CALIPSO missions.
Andreas Walbröl, Hannes J. Griesche, Mario Mech, Susanne Crewell, and Kerstin Ebell
Atmos. Meas. Tech., 17, 6223–6245, https://doi.org/10.5194/amt-17-6223-2024, https://doi.org/10.5194/amt-17-6223-2024, 2024
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We developed retrievals of integrated water vapour (IWV), temperature profiles, and humidity profiles from ground-based passive microwave remote sensing measurements gathered during the MOSAiC expedition. We demonstrate and quantify the benefit of combining low- and high-frequency microwave radiometers to improve humidity profiling and IWV estimates by comparing the retrieved quantities to single-instrument retrievals and reference datasets (radiosondes).
Tessa E. Rosenberger, Thijs Heus, Girish N. Raghunathan, David D. Turner, Timothy J. Wagner, and Julia M. Simonson
EGUsphere, https://doi.org/10.5194/egusphere-2024-2894, https://doi.org/10.5194/egusphere-2024-2894, 2024
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Entrainment is key in understanding temperature and moisture changes within the boundary layer, but it is difficult to observe using ground-based observations. This work used simulations to verify an assumption that simplifies entrainment estimations from ground-based observational data, recognizing that entrainment is the combination of the transfer of heat and moisture from above the boundary layer into it and the change in concentration of heat and moisture as boundary layer depth changes.
Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum
Atmos. Meas. Tech., 17, 6025–6046, https://doi.org/10.5194/amt-17-6025-2024, https://doi.org/10.5194/amt-17-6025-2024, 2024
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The amount of sunlight reflected by the Earth’s surface (albedo) is vital for the Earth's radiative system. While satellite instruments offer detailed spatial and temporal albedo maps, they only cover seven wavelength bands. We generate albedo maps that fully span the visible and near-infrared range using a machine learning algorithm. These maps reveal how the reflectivity of different land surfaces varies throughout the year. Our dataset enhances the understanding of the Earth's energy balance.
Dianrun Zhao, Shanshan Du, Chu Zou, Longfei Tian, Meng Fan, Yulu Du, and Liangyun Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3118, https://doi.org/10.5194/egusphere-2024-3118, 2024
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The TanSat-2 satellite is designed for global carbon monitoring. It provides high-resolution, dual-band observations of solar-induced chlorophyll fluorescence, a key indicator of plant photosynthesis. Through simulations, we optimized the satellite's data processing and found it can retrieve this fluorescence with great accuracy. These findings suggest that TanSat-2 will enhance global monitoring of carbon cycles and vegetation health, offering valuable insights for climate change research.
Sebastian Rhode, Peter Preusse, Jörn Ungermann, Inna Polichtchouk, Kaoru Sato, Shingo Watanabe, Manfred Ern, Karlheinz Nogai, Björn-Martin Sinnhuber, and Martin Riese
Atmos. Meas. Tech., 17, 5785–5819, https://doi.org/10.5194/amt-17-5785-2024, https://doi.org/10.5194/amt-17-5785-2024, 2024
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We investigate the capabilities of a proposed satellite mission, CAIRT, for observing gravity waves throughout the middle atmosphere and present the necessary methodology for in-depth wave analysis. Our findings suggest that such a satellite mission is highly capable of resolving individual wave parameters and could give new insights into the role of gravity waves in general atmospheric circulation and atmospheric processes.
Xiaoye Wang, Jing Xu, Songhua Wu, Qichao Wang, Guangyao Dai, Peizhi Zhu, Zhizhong Su, Sai Chen, Xiaomeng Shi, and Mengqi Fan
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-156, https://doi.org/10.5194/amt-2024-156, 2024
Revised manuscript accepted for AMT
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In this paper, we proposed a data fusion method to obtain the no-blind zone wind speed profiles covering the whole Atmospheric Boundary Layer based on the joint measurements of coherent Doppler lidar (CDL), radar wind profiler (RWP) and automatic weather station (AWS). Since above instruments are widely deployed in China, we believe this method has broad application prospects on the improvement of the boundary layer parameterization scheme in numerical forecast models.
Suyoung Sim, Sungwon Choi, Daeseong Jung, Jongho Woo, Nayeon Kim, Sungwoo Park, Honghee Kim, Ukkyo Jeong, Hyunkee Hong, and Kyung-Soo Han
Atmos. Meas. Tech., 17, 5601–5618, https://doi.org/10.5194/amt-17-5601-2024, https://doi.org/10.5194/amt-17-5601-2024, 2024
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This study evaluates the use of background surface reflectance (BSR) derived from a semi-empirical bidirectional reflectance distribution function (BRDF) model based on GEMS satellite images. Analysis shows that BSR provides improved accuracy and stability compared to Lambertian-equivalent reflectivity (LER). These results indicate that BSR can significantly enhance climate analysis and air quality monitoring, making it a promising tool for accurate environmental satellite applications.
Daniel Durbin, Yadong Wang, and Pao-Liang Chang
Atmos. Meas. Tech., 17, 5397–5411, https://doi.org/10.5194/amt-17-5397-2024, https://doi.org/10.5194/amt-17-5397-2024, 2024
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A method for determining drop size distributions (DSDs) for rain using radar measurements from two frequencies at two polarizations is presented. Following some preprocessing and quality control, radar measurements are incorporated into a model that uses swarm intelligence to seek the most suitable DSD to produce the input measurements.
Witali Krochin, Axel Murk, and Gunter Stober
Atmos. Meas. Tech., 17, 5015–5028, https://doi.org/10.5194/amt-17-5015-2024, https://doi.org/10.5194/amt-17-5015-2024, 2024
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Atmospheric tides are global-scale oscillations with periods of a fraction of a day. Their observation in the middle atmosphere is challenging and rare, as it requires continuous measurements with a high temporal resolution. In this paper, temperature time series of a ground-based microwave radiometer were analyzed with a spectral filter to derive thermal tide amplitudes and phases in an altitude range of 25–50 km at the geographical locations of Payerne and Bern (Switzerland).
Matteo Ottaviani, Gabriel Harris Myers, and Nan Chen
Atmos. Meas. Tech., 17, 4737–4756, https://doi.org/10.5194/amt-17-4737-2024, https://doi.org/10.5194/amt-17-4737-2024, 2024
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We analyze simulated polarization observations over snow to investigate the capabilities of remote sensing to determine surface and atmospheric properties in snow-covered regions. Polarization measurements are demonstrated to aid in the determination of snow grain shape, ice crystal roughness, and the vertical distribution of impurities in the snow–atmosphere system, data that are critical for estimating snow albedo for use in climate models.
Yudong Gao, Lidou Huyan, Zheng Wu, and Bojun Liu
Atmos. Meas. Tech., 17, 4675–4686, https://doi.org/10.5194/amt-17-4675-2024, https://doi.org/10.5194/amt-17-4675-2024, 2024
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A symmetric error model built by symmetric rain rates handles the non-Gaussian error structure of the reflectivity error. The accuracy and linearization of rain rates can further improve the Gaussianity.
José Alex Zenteno-Hernández, Adolfo Comerón, Federico Dios, Alejandro Rodríguez-Gómez, Constantino Muñoz-Porcar, Michaël Sicard, Noemi Franco, Andreas Behrendt, and Paolo Di Girolamo
Atmos. Meas. Tech., 17, 4687–4694, https://doi.org/10.5194/amt-17-4687-2024, https://doi.org/10.5194/amt-17-4687-2024, 2024
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We study how the spectral characteristics of a solid-state laser in an atmospheric temperature profiling lidar using the Raman technique impact the temperature retrieval accuracy. We find that the spectral widening, with respect to a seeded laser, has virtually no impact, while crystal-rod temperature variations in the laser must be kept within a range of 1 K for the uncertainty in the atmospheric temperature below 1 K. The study is carried out through spectroscopy simulations.
Robert Reichert, Natalie Kaifler, and Bernd Kaifler
Atmos. Meas. Tech., 17, 4659–4673, https://doi.org/10.5194/amt-17-4659-2024, https://doi.org/10.5194/amt-17-4659-2024, 2024
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Imagine you want to determine how quickly the pitch of a passing ambulance’s siren changes. If the vehicle is traveling slowly, the pitch changes only slightly, but if it is traveling fast, the pitch also changes rapidly. In a similar way, the wind in the middle atmosphere modulates the wavelength of atmospheric gravity waves. We have investigated the question of how strong the maximum wind may be so that the change in wavelength can still be determined with the help of wavelet transformation.
Qiang Guo, Yuning Liu, Xin Wang, and Wen Hui
Atmos. Meas. Tech., 17, 4613–4627, https://doi.org/10.5194/amt-17-4613-2024, https://doi.org/10.5194/amt-17-4613-2024, 2024
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Non-linearity (NL) correction is a critical procedure to guarantee that the calibration accuracy of a spaceborne sensor approaches a reasonable level. Different from the classical method, a new NL correction method for a spaceborne Fourier transform spectrometer is proposed. To overcome the inaccurate linear coefficient from two-point calibration influencing NL correction, an iteration algorithm is established that is suitable for NL correction of both infrared and microwave sensors.
Bing Cao, Jennifer S. Haase, Michael J. Murphy Jr., and Anna M. Wilson
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-119, https://doi.org/10.5194/amt-2024-119, 2024
Revised manuscript accepted for AMT
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This paper describes an Airborne Radio Occultation (ARO) observation system installed on reconnaissance aircraft that uses GPS signal refraction in the atmosphere to retrieve information about the temperature and moisture in the storm environment as far away as 400 km surrounding the flight track. The characteristics and quality of 1700 ARO refractivity profiles were assessed. These observations are collected to help understand atmospheric rivers and improve their forecasting.
Yuanxin Pan, Grzegorz Kłopotek, Laura Crocetti, Rudi Weinacker, Tobias Sturn, Linda See, Galina Dick, Gregor Möller, Markus Rothacher, Ian McCallum, Vicente Navarro, and Benedikt Soja
Atmos. Meas. Tech., 17, 4303–4316, https://doi.org/10.5194/amt-17-4303-2024, https://doi.org/10.5194/amt-17-4303-2024, 2024
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Crowdsourced smartphone GNSS data were processed with a dedicated data processing pipeline and could produce millimeter-level accurate estimates of zenith total delay (ZTD) – a critical atmospheric variable. This breakthrough not only demonstrates the feasibility of using ubiquitous devices for high-precision atmospheric monitoring but also underscores the potential for a global, cost-effective tropospheric monitoring network.
Almudena Velázquez Blázquez, Edward Baudrez, Nicolas Clerbaux, and Carlos Domenech
Atmos. Meas. Tech., 17, 4245–4256, https://doi.org/10.5194/amt-17-4245-2024, https://doi.org/10.5194/amt-17-4245-2024, 2024
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The Broadband Radiometer measures shortwave and total-wave radiances filtered by the spectral response of the instrument. To obtain unfiltered solar and thermal radiances, the effect of the spectral response needs to be corrected for, done within the BM-RAD processor. Errors in the unfiltering are propagated into fluxes; thus, accurate unfiltering is required for their proper estimation (within BMA-FLX). Unfiltering errors are estimated to be <0.5 % for the shortwave and <0.1 % for the longwave.
Qihou Zhou, Yanlin Li, and Yun Gong
Atmos. Meas. Tech., 17, 4197–4209, https://doi.org/10.5194/amt-17-4197-2024, https://doi.org/10.5194/amt-17-4197-2024, 2024
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We discuss several robust estimators to compute the variance of a normally distributed random variable to deal with interference. Compared to rank-based estimators, the methods based on the geometric mean are more accurate and are computationally more efficient. We apply three robust estimators to incoherent scatter power and velocity processing, along with the traditional sample mean estimator. The best estimator is a hybrid estimator that combines the sample mean and a robust estimator.
Zhao Shi, Yuxiang Wen, and Jianxin He
Atmos. Meas. Tech., 17, 4121–4135, https://doi.org/10.5194/amt-17-4121-2024, https://doi.org/10.5194/amt-17-4121-2024, 2024
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The squall line is a type of convective system. Squall lines are often associated with damaging weather, so identifying and tracking squall lines plays an important role in early meteorological disaster warnings. A clustering-based method is proposed in this article. It can identify the squall lines within the radar scanning range with an accuracy rate of 95.93 %. It can also provide the three-dimensional structure and movement tracking results for each squall line.
Elizabeth N. Smith and Jacob T. Carlin
Atmos. Meas. Tech., 17, 4087–4107, https://doi.org/10.5194/amt-17-4087-2024, https://doi.org/10.5194/amt-17-4087-2024, 2024
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Boundary-layer height observations remain sparse in time and space. In this study we create a new fuzzy logic method for synergistically combining boundary-layer height estimates from a suite of instruments. These estimates generally compare well to those from radiosondes; plus, the approach offers near-continuous estimates through the entire diurnal cycle. Suspected reasons for discrepancies are discussed. The code for the newly presented fuzzy logic method is provided for the community to use.
Laura Bianco, Bianca Adler, Ludovic Bariteau, Irina V. Djalalova, Timothy Myers, Sergio Pezoa, David D. Turner, and James M. Wilczak
Atmos. Meas. Tech., 17, 3933–3948, https://doi.org/10.5194/amt-17-3933-2024, https://doi.org/10.5194/amt-17-3933-2024, 2024
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The Tropospheric Remotely Observed Profiling via Optimal Estimation physical retrieval is used to retrieve temperature and humidity profiles from various combinations of passive and active remote sensing instruments, surface platforms, and numerical weather prediction models. The retrieved profiles are assessed against collocated radiosonde in non-cloudy conditions to assess the sensitivity of the retrievals to different input combinations. Case studies with cloudy conditions are also inspected.
Björn Linder, Peter Preusse, Qiuyu Chen, Ole Martin Christensen, Lukas Krasauskas, Linda Megner, Manfred Ern, and Jörg Gumbel
Atmos. Meas. Tech., 17, 3829–3841, https://doi.org/10.5194/amt-17-3829-2024, https://doi.org/10.5194/amt-17-3829-2024, 2024
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The Swedish research satellite MATS (Mesospheric Airglow/Aerosol Tomography and Spectroscopy) is designed to study atmospheric waves in the mesosphere and lower thermosphere. These waves perturb the temperature field, and thus, by observing three-dimensional temperature fluctuations, their properties can be quantified. This pre-study uses synthetic MATS data generated from a general circulation model to investigate how well wave properties can be retrieved.
Gia Huan Pham, Shu-Chih Yang, Chih-Chien Chang, Shu-Ya Chen, and Cheng Yung Huang
Atmos. Meas. Tech., 17, 3605–3623, https://doi.org/10.5194/amt-17-3605-2024, https://doi.org/10.5194/amt-17-3605-2024, 2024
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This research examines the characteristics of low-level GNSS radio occultation (RO) refractivity bias over ocean and land and its dependency on the RO retrieval uncertainty, atmospheric temperature, and moisture. We propose methods for estimating the region-dependent refractivity bias. Our methods can be applied to calibrate the refractivity bias under different atmospheric conditions and thus improve the applications of the GNSS RO data in the deep troposphere.
Sebastiano Padovan, Axel Von Engeln, Saverio Paolella, Yago Andres, Chad R. Galley, Riccardo Notarpietro, Veronica Rivas Boscán, Francisco Sancho, Francisco Martin Alemany, Nicolas Morew, and Christian Marquardt
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-80, https://doi.org/10.5194/amt-2024-80, 2024
Revised manuscript accepted for AMT
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Radio Occultation (RO) measurements are an important contribution to numerical weather predictions and long-term climate studies. Using more than a hundred thousand occultations recorded by instruments onboard the Sentinel-6A and Cosmic-2 satellites, this work studies the effects of the clock data rate of the Global Navigation Satellite System on the RO data quality. GLONASS occultations benefit of high-rate clock data (1 second), GPS occultation have high quality already at 30 seconds.
Sanja Dmitrovic, Johnathan W. Hair, Brian L. Collister, Ewan Crosbie, Marta A. Fenn, Richard A. Ferrare, David B. Harper, Chris A. Hostetler, Yongxiang Hu, John A. Reagan, Claire E. Robinson, Shane T. Seaman, Taylor J. Shingler, Kenneth L. Thornhill, Holger Vömel, Xubin Zeng, and Armin Sorooshian
Atmos. Meas. Tech., 17, 3515–3532, https://doi.org/10.5194/amt-17-3515-2024, https://doi.org/10.5194/amt-17-3515-2024, 2024
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This study introduces and evaluates a new ocean surface wind speed product from the NASA Langley Research Center (LARC) airborne High-Spectral-Resolution Lidar – Generation 2 (HSRL-2) during the NASA ACTIVATE mission. We show that HSRL-2 surface wind speed data are accurate when compared to ground-truth dropsonde measurements. Therefore, the HSRL-2 instrument is able obtain accurate, high-resolution surface wind speed data in airborne field campaigns.
Cited articles
Aires, F., Aznay, O., Prigent, C., Paul, M., and Bernardo F.: Synergistic multi-wavelength remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp-A, J. Geophys. Res., 117, D18304, https://doi.org/10.1029/2011JD017188, 2012.
Anders, U. and Korn, O.: Model selection in neural networks, Neural Networks, 12, 309–323, 1999.
Bellerby, T., Todd, M., Kniveton, D., and Kidd, C.,: Rainfall Estimation from a Combination of TRMM Precipitation Radar and GOES Multispectral Satellite Imagery through the Use of an Artificial Neural Network, J. Appl. Meteorol., 39, 2115–2128, https://doi.org/10.1175/1520-0450(2001)040<2115:REFACO>2.0.CO;2, 2000.
Bellerby, T. J.: Satellite rainfall uncertainty estimation using an artificial neural network, J. Hydrometeorol., 8, 1397–1412, https://doi.org/10.1175/2007JHM846.1, 2007.
Bennartz, R. and Bauer, P.: Sensitivity of microwave radiances at 85-183 GHz to precipitating ice particles, Radio Sci., 38, 8075, https://doi.org/10.1029/2002RS002626, 2003.
Bennartz, R. and Petty, G. W.: The sensitivity of microwave remote sensing observations of precipitation to ice particle size distributions, J. Appl. Meteorol., 40, 345–364, https://doi.org/10.1175/1520-0450(2001)040<0345:TSOMRS>2.0.CO;2, 2001.
Blackwell, W. J. and Chen, F. W.: Neural network applications in high-resolution atmospheric remote sensing, Lincoln Lab. J., 15, 299–322, 2005.
Boukabara, S.-A., Garrett, K., and Blackwell, B.: ATMS Description & Expected Performances, Post-EPS User Consultation Workshop, Darmstadt, Germany, 29–30 September 2011.
Boukabara, S.-A., Garrett, K., Grassotti, C., Iturbide-Sanchez, F., Chen, W., Jiang, Z., Clough, S. A., Zhan, X., Liang, P., Liu, Q,. Islam, T., Zubko, V., and Mims, A.: A physical approach for a simultaneous retrieval of sounding, surface, hydrometeor, and cryospheric parameters from SNPP/ATMS, J. Geophys. Res.-Atmos., 118, 12600–12619, https://doi.org/10.1002/2013JD020448, 2013.
Burns, B. A., Wu, X., and Diak, G. R.: Effects of precipitation and cloud ice on brightness temperatures in AMSU moisture channels, IEEE T. Geosci. Remote, 35, 1429–1437, https://doi.org/10.1109/36.649797, 1997.
Casella, D., Panegrossi, G., Sanò, P., Mugnai, A., Smith, E. A., Tripoli, G. J., Dietrich, S., Formenton, M., Leung, W. Y., and Mehta, A.: Transitioning from CRD to CDRD in bayesian retrieval of rainfall from satellite passive microwave measurements: Part 2. Overcoming database profile selection ambiguity by consideration of meteorological control on microphysics, IEEE T. Geosci. Remote, 51, 4650–4671, 2013.
Casella, D., Panegrossi, G., Sanò, P., Milani, L., Petracca, M., and Dietrich, S.: A novel algorithm for detection of precipitation in tropical regions using PMW radiometers, Atmos. Meas. Tech., 8, 1217–1232, https://doi.org/10.5194/amt-8-1217-2015, 2015.
Chandrasekar, V., Le, M., and Awaka, J.: Vertical profile classification algorithm for GPM, Int. Geosci. Remote Se., IGARSS 2014, 3458–3761, https://doi.org/10.1109/IGARSS.2014.6947301, 2014.
Chen, F. W. and Staelin, D. H.: AIRS/AMSU/HSB precipitation estimates, IEEE T. Geosci. Remote, 41, 410–417, https://doi.org/10.1109/TGRS.2002.808322, 2003.
Chen, F. W., Bickmeier, L. J., Blackwell, W. J., Jairam, L. G., and Leslie, V. R.: Neural network retrieval of precipitation using NPOESS microwave sensors, Int. Geosci. Remote Se., IGARSS 2007, 2272–2275, https://doi.org/10.1109/IGARSS.2007.4423294, 2007.
Chen, Y., Aires, F., Francis, J. A., and Miller, J. R.: Observed relationships between artic longwave cloud forcing and cloud parameters using a neural network, J. Climate, 4087–4104, 2006.
Coulibaly, P., Dibike, Y. B., and Anctil, F.: Downscaling Precipitation and Temperature with Temporal Neural Networks, J. Hydrometeorol., 6, 483–496, https://doi.org/10.1175/JHM409.1, 2005.
Del Frate, F. and Schiavon, G.: Nonlinear principal component analysis for the radiometric inversion of atmospheric profiles by using neural networks, IEEE T. Geosci. Remote, 37, 2335–2342, https://doi.org/10.1109/36.789630, 1999.
Draper, D., Newell, D., Wentz, F., Krimchansky, S., and Skofronick-Jackson, G.: The Global Precipitation Measurement (GPM) Microwave Imager (GMI): Instrument Overview and Early On-Orbit Performance, IEEE J. Sel. Top. Appl., 8, 3452–3462, https://doi.org/10.1109/JSTARS.2015.2403303, 2015.
Ferraro, R. R. and Marks, G. F.: The development of SSM/I rain-rate retrieval algorithms using ground-based radar measurements, J. Atmos. Ocean. Tech., 12, 755–770, https://doi.org/10.1175/1520-0426(1995)012<0755:TDOSRR>2.0.CO;2, 1995.
Ferraro, R. R.: The Status of the NOAA/NESDIS Operational AMSU Precipitation Algorithm, 2nd Workshop of the International Precipitation Working Group, Monterey, 9 pp., 2004.
Ferraro, R. R., Weng, F., Grody, N. C., Zhao, L., Meng, H., Kongoli, C., Pellegrino, P., Qiu, S., and Dean, C.: NOAA operational hydrological products derived from the advanced microwave sounding unit, IEEE T. Geosci. Remote, 43 1036–1049, 2005.
Funatsu, B. M., Claud, C., and Chaboureau, J.-P.: Potential of Advanced Microwave Sounding Unit to identify precipitating systems and associated upper-level features in the Mediterranean region: Case studies, J. Geophys. Res., 112, D17113, https://doi.org/10.1029/2006JD008297, 2007.
Funatsu, B. M., Claud, C., and Chaboureau, J.-P.: Comparison between the large-scale environments of moderate and intense precipitating systems in the Mediterranean region, Mon. Weather Rev., 137, 3933–3959, https://doi.org/10.1175/2009MWR2922.1, 2009.
Goldberg, M. D., Kilcoyne, H., Cikanek, H., and Mehta, A.: Joint Polar Satellite System: The United States next generation civilian polar-orbiting environmental satellite system, J. Geophys. Res.-Atmos., 118, 13463–13475, https://doi.org/10.1002/2013JD020389, 2013.
Grody, N. C.: Classification of snow cover and precipitation using the special sensor microwave imager, J. Geophys. Res., 96, 7423–7435, https://doi.org/10.1029/91JD00045, 1991.
Hall, T., Brooks, H. E., and Doswell III, C. A.: Precipitation forecasting using a neural network, Weather Forecast., 14, 338–345, https://doi.org/10.1175/1520-0434(1999)014<0338:PFUANN>2.0.CO;2, 1999.
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. Tech., 33, 653–667, https://doi.org/10.1175/JTECH-D-15-0097.1, 2016.
Haupt, S. E., Pasini, A., and Marzban, C.: Artificial Intelligence Methods in the Environmental Sciences, Springer, ISBN 978-1-4020-9117-9 (HB), 2009.
Heymsfield, G. M., Geerts, B., and Tian, L.: TRMM Precipitation Radar Reflectivity Profiles as Compared with High-Resolution Airborne and Ground-Based Radar Measurements, J. Appl. Meteorol., 39, 2080–2102, https://doi.org/10.1175/1520-0450(2001)040<2080:TPRRPA>2.0.CO;2, 2000.
Hirose, M., Shimizu, S., Oki, R., Iguchi, T., Short, D. A., and Nakamura, K.: Incidence-Angle Dependency of TRMM PR Rain Estimates, J. Atmos. Ocean. Tech., 29, 192–206, https://doi.org/10.1175/JTECH-D-11-00067.1, 2012.
Hong, G., Heygster, G., Miao, J., and Kunzl, K.: Detection of tropical deep convective clouds from AMSU-B vater vapor channels measurements, J. Geophys. Res., 110, D05205, https://doi.org/10.1029/2004JD004949, 2005.
Hong, Y., Hsu, K.-L., Sorooshian, S., and Gao, X.: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system, J. Appl. Meteorol., 43, 1834–1853, 2004.
Hong, G., Heygster, G., Notholt, J., and Buehler, S. A.: Interannual to Diurnal Variations in Tropical and Subtropical Deep Convective Clouds and Convective Overshooting from Seven Years of AMSU-B Measurements, J. Climate, 21, 4168–4189, https://doi.org/10.1175/2008JCLI1911.1, 2008.
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The global precipitation measurement mission, B. Am. Meteorol. Soc., 95, 701–722, https://doi.org/10.1175/BAMS-D-13-00164.1, 2014.
Hsu, K.-L., Gao, X., Sorooshian, S., and Gupta, H. V.: Precipitation estimation from remotely sensed information using artificial neural networks, J. Appl. Meteorol., 36, 1176–1190, https://doi.org/10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2, 1997.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/JHM560.1, 2007.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E. J., and Pingping, X.: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), ATBD v. 4.5, NASA, available at: http://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_ V4.5.pdf (last access: 1 November 2016), 66 pp., 2015.
Iguchi, T., Kozu, T., Meneghini, R., Awaka, J., and Okamoto, K.: Rain-Profiling Algorithm for the TRMM Precipitation Radar, J. Appl. Meteorol., 39, 2038–2052, https://doi.org/10.1175/1520-0450(2001)040<2038:RPAFTT>2.0.CO;2, 2000.
Iguchi, T., Kozu, T., Kwiatkowski, J., Meneghini, R., Awaka, J., and Okamoto, K.: Uncertainties in the Rain Profiling Algorithm for the TRMM Precipitation Radar, J. Meteorol. Soc. Jpn., 87A, 1–30, https://doi.org/10.2151/jmsj.87A.1, 2009.
Iturbide-Sanchez, F., Boukabara, S.-A., Chen, R., Garrett, K., Grassotti, C., Chen, W., and Weng, F.: Assessment of a Variational Inversion System for Rainfall Rate Over Land and Water Surfaces, IEEE T. Geosci. Remote, 49, 3311–3333, https://doi.org/10.1109/TGRS.2011.2119375, 2011.
Kidd, C.: On rainfall retrieval using polarization-corrected temperatures, Int. J. Remote Sens., 19, 981–996, https://doi.org/10.1080/014311698215829, 1998.
Kidd, C., Matsui, T., Chern, J., Mohr, K., Kummerow, C., and Randel, D.: Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically Based Retrieval Scheme, J. Hydrometeorol., 17, 383–400, https://doi.org/10.1175/JHM-D-15-0051.1, 2016.
Kirstetter, P. E., Hong, Y., Gourley, J. J., Chen, S., Flamig, Z., Zhang, J., Schwaller, M., Petersen, W., and Amitai, E.: Toward a Framework for Systematic Error Modeling of Spaceborne Precipitation Radar with NOAA/NSSL Ground Radar–Based National Mosaic QPE, J. Hydrometeorol., 13, 1285–1300, https://doi.org/10.1175/JHM-D-11-0139.1, 2012.
Kirstetter, P.-E., Viltard, N., and Gosset, M.: An error model for instantaneous satellite rainfall estimates: evaluation of BRAIN-TMI over West Africa, Q. J. Roy. Meteor. Soc., 139, 894–911, https://doi.org/10.1002/qj.1964, 2013.
Kongoli, C., Meng, H., Dong, J., and Ferraro, R.: A snowfall detection algorithm over land utilizing high-frequency passive microwave measurements – Application to ATMS. J. Geophys. Res.-Atmos., 120, 1918–1932, https://doi.org/10.1002/2014JD022427, 2015.
Krasnopolsky, V. M., Fox-Rabinovitz, M. S., and Belochitski, A. A.: Decadal climate simulations using accurate and fast neural network emulation of full, longwave and shortwave, radiation, Mon. Weather Rev., 3683–3695, https://doi.org/10.1175/2008MWR2385.1, 2008.
Kummerow, C. D., Ringerud, S., Crook, J., Randel, D., and Berg, W.: An Observationally Generated A Priori Database for Microwave Rainfall Retrievals, J. Atmos. Ocean. Tech., 28, 113–130, https://doi.org/10.1175/2010JTECHA1468.1, 2011.
Kummerow, C. D., Randel, D. L., Kulie, M., Wang, N.-Y., Ferraro, R., Munchak, S. J., and Petkovic, V.: The Evolution of the Goddard Profiling Algorithm to a Fully Parametric Scheme, J. Atmos. Ocean. Tech., 32, 2265–2280, https://doi.org/10.1175/JTECH-D-15-0039.1, 2015.
Laviola, S. and Levizzani, V.: The 183-WLS fast rain rate retrieval algorithm. Part I: Retrieval design, Atmos. Res., 99, 443–461, https://doi.org/10.1016/j.atmosres.2010.11.013, 2011.
Le, M. and Chandrasekar, V.: Hydrometeor Profile Characterization Method for Dual-Frequency Precipitation Radar Onboard the GPM, IEEE T. Geosci. Remote, 51, 3648–3658, https://doi.org/10.1109/TGRS.2012.2224352, 2013a.
Le, M. and Chandrasekar, V.: Precipitation Type Classification Method for Dual-Frequency Precipitation Radar (DPR) Onboard the GPM, IEEE T. Geosci. Remote, 51, 1784–1790, https://doi.org/10.1109/TGRS.2012.2205698, 2013b.
Leslie, R. V., Blackwell, W. J., Bickmeier, L. J., and Jaram, L. G.: Neural network microwave precipitation retrievals and modeling results, Proc. SPIE, 7154, 715406–715408, https://doi.org/10.1117/12.804815, 2008.
Liao, L., Meneghini, R., and Iguchi, T.: Comparisons of rain rate and reflectivity factor derived from the TRMM Precipitation Radar and the WSR-88D over the Melbourne, Florida site, J. Atmos. Ocean. Tech., 18, 1959–1974, https://doi.org/10.1175/1520-0426(2001)018<1959:CORRAR>2.0.CO;2, 2001.
Liao, L., Meneghini, R., and Tokay, A.: Uncertainties of GPM DPR Rain Estimates Caused by DSD Parameterizations, J. Appl. Meteorol. Clim., 53, 2524–2537, https://doi.org/10.1175/JAMC-D-14-0003.1, 2014.
Lin, X. and Hou, A. Y.: Evaluation of coincident passive microwave estimates using TRRM PR and ground measurements as references, J. Appl. Meteorol. Clim., 47, 3170–3187, https://doi.org/10.1175/2008JAMC1893.1, 2008.
Liou, Y.-A., Tzeng, Y. C., and Chen, K. S.: A neural-network approach to radiometric sensing of land-surface parameters, IEEE T. Geosci. Remote, 37, 2718–2724, https://doi.org/10.1109/36.803419, 1999.
Mahesh, C., Satya Prakash, Sathiyamoorthy, V., and Gairola, R. M.: Artificial neural network based microwave precipitation estimation using scattering index and polarization corrected temperature, Atmos. Res., 102, 358–364, https://doi.org/10.1016/j.atmosres.2011.09.003, 2011.
Marzban, C.: Neural Networks for Postprocessing Model Output: ARPS, Mon. Weather Rev., 131, 1103–1111, https://doi.org/10.1175/1520-0493(2003)131<1103:NNFPMO>2.0.CO;2, 2003.
Marzban, C.: Basic statistics and basic AI: neural networks, in: Artificial Intelligence Methods in the Environmental Science, edited by: Haupt, S. E., Pasini, A., and Marzban C., Springer, 15–47, 2009.
Mugnai, A., Smith, E. A., and Tripoli, G. J.: Foundations for statistical physical precipitation retrieval from passive microwave satellite measurement. Part II : Emission-source and generalized weighting-function properties of a time-dependent cloud-radiation model, J. Appl. Meteorol, 32, 17–39, 1993.
Mugnai, A., Casella, D., Cattani, E., Dietrich, S., Laviola, S., Levizzani, V., Panegrossi, G., Petracca, M., Sanò, P., Di Paola, F., Biron, D., De Leonibus, L., Melfi, D., Rosci, P., Vocino, A., Zauli, F., Pagliara, P., Puca, S., Rinollo, A., Milani, L., Porcù, F., and Gattari, F.: Precipitation products from the hydrology SAF, Nat. Hazards Earth Syst. Sci., 13, 1959–1981, https://doi.org/10.5194/nhess-13-1959-2013, 2013a.
Mugnai, A., Smith, E. A., Tripoli, G. J., Bizzarri, B., Casella, D., Dietrich, S., Di Paola, F., Panegrossi, G., and Sanò, P.: CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations, Nat. Hazards Earth Syst. Sci., 13, 887–912, https://doi.org/10.5194/nhess-13-887-2013, 2013b.
NASA: STORM-Precipitation Processing System (PPS), available at: https://storm-pps.gsfc.nasa.gov/, ftp://pps.gsfc.nasa.gov/pub/, and ftp://arthurhou.pps.eosdis.nasa.gov, last access: 10 November 2016.
Newell, D., Draper, D., Figgins, D., Berdanier, B., Kubitschek, M., Holshouser, D., Sexton, A., Krimchansky, S., Wentz, F., and Meissner, T.: GPM microwave imager key performance and calibration results, Int. Geosci. Remote Se., IGARSS 2014, 3754–3757, https://doi.org/10.1109/IGARSS.2014.6947300, 2014.
NOAA: Comprehensive Large Array-data Stewardship System (CLASS), JPSS Advanced Technology Microwave Sounder Sensor Data Record (ATMS_SDR), available at: http://www.nsof.class.noaa.gov/saa/products/search?sub_id=0&datatype_family=ATMS_SDR&submit.x=15&submit.y=8, last access: 10 November 2016.
Panegrossi, G., Dietrich, S., Marzano, F. S., Mugnai, A., Smith, E. A., Xiang, X., Tripoli, G. J., Wang, P. K., and Poiares Baptista, J. V. P.: Use of cloud model microphysics for passive microwave-based precipitation retrieval: significance of consistency between model and measurement manifolds, J. Atmos. Sci., 55, 1644–1673, 1998.
Panegrossi, G., Casella, D., Dietrich, S., Marra, A. C., Milani, L., Petracca, M., Sanò, P., and Mugnai, A.: CDRD and PNPR passive microwave precipitation retrieval algorithms: extension to the MSG full disk area, Proc. 2014 EUMETSAT Meteorological Satellite Conference, Geneva, https://www.eumetsat.int/website/home/News/ConferencesandEvents/DAT_2076129.html (last access: 1 November 2016), 2014.
Panegrossi, G., Casella, D., Dietrich, S., Marra, A.C., Petracca, M., Sanò, P., Baldini, L., Roberto, N., Adirosi, E., Cremonini, R., Bechini, R., and Vulpiani, G.: Use of the constellation of PMW radiometers in the GPM ERA for heavy precipitation event monitoring and analysis during fall 2014 in Italy, Int. Geosci. Remote Se., IGARSS 2015, 5150–5153, https://doi.org/10.1109/IGARSS.2015.7326993, 2015.
Panegrossi, G., Casella, D., Dietrich, S., Marra, A.C., Sanò, P., Mugnai, A., Baldini, L., Roberto, N., Adirosi, E., Cremonini, R., Bechini, R., Vulpiani, G., Petracca, M., and Porcù, F.: Use of the GPM Constellation for Monitoring Heavy Precipitation Events Over the Mediterranean Region, IEEE J. Sel. Top. Appl., 9, 2733–2763, https://doi.org/10.1109/JSTARS.2016.2520660, 2016.
Petković, V. and Kummerow, C. D.: Performance of the GPM Passive Microwave Retrieval in the Balkan Flood Event of 2014, J. Hydrometeorol., 16, 2501–2518, https://doi.org/10.1175/JHM-D-15-0018.1, 2015.
Puca, S., Porcu, F., Rinollo, A., Vulpiani, G., Baguis, P., Balabanova, S., Campione, E., Ertürk, A., Gabellani, S., Iwanski, R., Jurašek, M., Kanák, J., Kerényi, J., Koshinchanov, G., Kozinarova, G., Krahe, P., Lapeta, B., Lábó, E., Milani, L., Okon, L'., Öztopal, A., Pagliara, P., Pignone, F., Rachimow, C., Rebora, N., Roulin, E., Sönmez, I., Toniazzo, A., Biron, D., Casella, D., Cattani, E., Dietrich, S., Di Paola, F., Laviola, S., Levizzani, V., Melfi, D., Mugnai, A., Panegrossi, G., Petracca, M., Sanò, P., Zauli, F., Rosci, P., De Leonibus, L., Agosta, E., and Gattari, F.: The validation service of the hydrological SAF geostationary and polar satellite precipitation products, Nat. Hazards Earth Syst. Sci., 14, 871–889, https://doi.org/10.5194/nhess-14-871-2014, 2014.
Qiu, S., Pellegrino, P., Ferraro, R., and Zhao, L.: The improved AMSU rain-rate algorithm and its evaluation for a cool season event in the Western United States, Weather Forecast., 20, 761–774, https://doi.org/10.1175/WAF880.1, 2005.
Sanò, P., Casella, D., Mugnai, A., Schiavon, G., Smith, E. A., and Tripoli, G. J.: Transitioning from CRD to CDRD in bayesian retrieval of rainfall from satellite passive microwave measurements: Part 1. Algorithm description and testing, IEEE T. Geosci. Remote, 51, 4119–4143, https://doi.org/10.1109/TGRS.2012.2227332, 2013.
Sanò, P., Panegrossi, G., Casella, D., Di Paola, F., Milani, L., Mugnai, A., Petracca, M., and Dietrich, S.: The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies, Atmos. Meas. Tech., 8, 837–857, https://doi.org/10.5194/amt-8-837-2015, 2015.
Sarma, D. K., Konwar, M., Sharma, S., Pal, S., Das, J., De, U. K., and Viswanathan, G.: An Artificial-Neural-Network-Based Integrated Regional Model for Rain Retrieval Over Land and Ocean, IEEE T. Geosci. Remote, 46, 1689–1696, https://doi.org/10.1109/TGRS.2008.916469, 2008.
Shank, D. B., Hoogenboom, G., and McClendon, R. W.: Dewpoint Temperature Prediction Using Artificial Neural Networks. J. Appl. Meteorol. Clim., 47, 1757–1769, https://doi.org/10.1175/2007JAMC1693.1, 2008.
Shimozuma, T. and Seto, S.: Evaluation of KUPR algorithm in matchup cases of GPM and TRMM, Int. Geosci. Remote Se., IGARSS 2015, 5134–5137, https://doi.org/10.1109/IGARSS.2015.7326989, 2015.
Schumacher, C. and Houze Jr., R. A.: Stratiform Rain in the Tropics as Seen by the TRMM Precipitation Radar, J. Climate, 16, 1739–1756, https://doi.org/10.1175/1520-0442(2003)016<1739:SRITTA>2.0.CO;2, 2003.
Shi, L.: Retrieval of Atmospheric Temperature Profiles from AMSU-A Measurement Using a Neural Network Approach, J. Atmos. Ocean. Tech., 18, 340–347, https://doi.org/10.1175/1520-0426(2001)018<0340:ROATPF>2.0.CO;2, 2001.
Smith, E. A., Leung, H. W.-Y., Elsner, J. B., Mehta, A. V., Tripoli, G. J., Casella, D., Dietrich, S., Mugnai, A., Panegrossi, G., and Sanò, P.: Transitioning from CRD to CDRD in Bayesian retrieval of rainfall from satellite passive microwave measurements: Part 3 – Identification of optimal meteorological tags, Nat. Hazards Earth Syst. Sci., 13, 1185–1208, https://doi.org/10.5194/nhess-13-1185-2013, 2013.
Sorooshian, S., Hsu, K. -L., Gao, X., Gupta, H. V., Imam, B., and Braithwaite, D.: Evaluation of PERSIANN system satellite–based estimates of tropical rainfall, B. Am. Meteorol. Soc., 81, 2035–2046, https://doi.org/10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2, 2000.
Staelin, D. H., Chen, F. W., and Fuentes, A.: Precipitation measurements using 183-GHz AMSU satellite observations, Int. Geosci. Remote Se., 4, 2069–2071, https://doi.org/10.1109/IGARSS.1999.775034, 1999.
Staelin, D. H. and Chen, F. W.: Precipitation observations near 54 and 183 GHz using the NOAA-15 satellite, IEEE T. Geosci. Remote, 38, 2322–2332, 2000.
Surussavadee, C. and Staelin, D. H.: Millimeter-wave precipitation retrievals and observed-vs.-simulated radiance distributions: sensitivity to assumptions, J. Atmos. Sci., 64, 3808–3826, 2007.
Surussavadee, C. and Staelin, D. H.: Global millimeter-wave precipitation retrievals trained with a cloud-resolving numerical weather prediction model, Part I: Retrieval design, IEEE T. Geosci. Remote, 46, 99–108, https://doi.org/10.1109/TGRS.2007.908302, 2008a.
Surussavadee, C. and Staelin, D. H.: Global millimeter-wave precipitation retrievals trained with a cloud-resolving numerical weather prediction model, Part II: Performance evaluation, IEEE T. Geosci. Remote, 46, 109–118, https://doi.org/10.1109/TGRS.2007.908299, 2008b.
Surussavadee, C. and Staelin, D. H.: Satellite Retrievals of Artic and Equatorial Rain and Snowfall Rates Using Millimeter Wavelengths, IEEE T. Geosci. Remote, 47, 3697–3707, 2009.
Surussavadee, C. and Staelin, D. H.: Global precipitation retrieval algorithm trained for SSMIS using a Numerical Weather Prediction Model: Design and evaluation, Int. Geosci. Remote Se., IGARSS 2010, 2341–2344, https://doi.org/10.1109/IGARSS.2010.5649699, 2010.
Surussavadee, C., Blackwell, W. J., Entekhabi, D., and Leslie, R. V.: A global precipitation retrieval algorithm for SUOMI NPP ATMS, Int. Geosci. Remote Se., IGARSS 2012, 1924–1927, https://doi.org/10.1109/IGARSS.2012.6351128, Munich, 2012.
Tang, L., Tian, Y., and Lin, X.: Validation of precipitation retrievals over land from satellite-based passive microwave sensors, J. Geophys. Res.-Atmos., 119, 4546–4567, https://doi.org/10.1002/2013JD020933, 2014.
Tapiador, F. J., Kidd, C., Levizzani, V., and Marzano, F. S.: A Neural Networks–Based Fusion Technique to Estimate Half-Hourly Rainfall Estimates at 0.1° Resolution from Satellite Passive Microwave and Infrared Data, J. Appl. Meteorol., 43, 576–594, https://doi.org/10.1175/1520-0450(2004)043<0576:ANNFTT>2.0.CO;2, 2004.
Tian, Y., Peters-Lidard, C. D., Eylander, J. B., Joyce, R. J., Huffman, G. J., Adler, R. F., Hsu, K., Turk, F. J., Garcia, M., and Zeng, J.: Component analysis of errors in satellite-based precipitation estimates, J. Geophys. Res., 114, D24101, https://doi.org/10.1029/2009JD011949, 2009.
Tian, Y., Nearing, G. S., Peters-Lidard, C. D., Harrison, K. W., and Tang, L.: Performance Metrics, Error Modeling, and Uncertainty Quantification, Mon. Weather Rev., 144, 607–613, https://doi.org/10.1175/MWR-D-15-0087.1, 2016.
Toyoshima, K., Masunaga, H., and Furuzawa, F. A.: Early Evaluation of Ku- and Ka-Band Sensitivities for the Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR), SOLA, 11, 14–17, https://doi.org/10.2151/sola.2015-004, 2015.
Tripoli, G. J.: A nonhydrostatic mesoscale model designed to simulate scale interaction, Mon. Weather Rev., 120, 1342–1359, 1992.
Tripoli, G. J. and Smith, E. A. Introducing Variable-Step Topography (VST) coordinates within dynamically constrained Nonhydrostatic Modeling Systems (NMW), part 1: VST formulation within NMS host model framework, Dynam. Atmos. Oceans, 66, 28–57, https://doi.org/10.1016/j.dynatmoce.2014.01.001, 2014a.
Tripoli, G. J. and Smith, E. A.: Introducing Variable-Step Topography (VST) coordinates within dynamically constrained Nonhydrostatic Modeling Systems (NMW), part 2: VST performance on orthodox obstacle flows, Dynam. Atmos. Oceans, 66, 10–27, https://doi.org/10.1016/j.dynatmoce.2014.01.003, 2014b.
Wang, J. R., Wilheit, T. T., and Chang, L. A.: Retrieval of total precipitable water using radiometric measurements near 92 and 183 GHz, J. Appl. Meteorol., 28, 146–154, https://doi.org/10.1175/1520-0450(1989)028<0146:ROTPWU>2.0.CO;2, 1989.
Wang, J. R., Zhan, J., and Racette, P.: Storm-associated microwave radiometric signatures in the frequency range of 90–220 GHz, J. Atmos. Ocean. Tech., 14, 13–31, https://doi.org/10.1175/1520-0426(1997)014<0013:SAMRSI>2.0.CO;2, 1997.
Weng, F., Zou, X., Wang, X., Yang, S., and Goldberg, M. D.: Introduction to Suomi national polar-orbiting partnership advanced technology microwave sounder for numerical weather prediction and tropical cyclone applications, J. Geophys. Res., 117, D19112, https://doi.org/10.1029/2012JD018144, 2012.
You, Y. and Liu, G.: The relationship between surface rainrate and water paths and its implications to satellite rainrate retrieval, J. Geophys. Res., 117, D13207, https://doi.org/10.1029/2012JD017662, 2012.
You, Y., Wang, N.-Y., and Ferraro, R.: A prototype precipitation retrieval algorithm over land using passive microwave observations stratified by surface condition and precipitation vertical structure, J. Geophys. Res.-Atmos., 120, 5295–5315. https://doi.org/10.1002/2014JD022534, 2015.
Zou, X., Weng, F., Zhang, B., Lin, L., Qin, Z., and Tallapragada, V.: Impacts of assimilation of ATMS data in HWRF on track and intensity forecasts of 2012 four landfall hurricanes, J. Geophys. Res.-Atmos., 118, 11558–11576, https://doi.org/10.1002/2013JD020405, 2013.
Short summary
The objective of this paper is to describe the development and evaluate the performance of a totally new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track ATMS radiometer measurements.
The objective of this paper is to describe the development and evaluate the performance of a...
