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.
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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Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi
Atmos. Meas. Tech., 17, 2195–2217, https://doi.org/10.5194/amt-17-2195-2024, https://doi.org/10.5194/amt-17-2195-2024, 2024
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The paper describes a new machine-learning-based snowfall retrieval algorithm for Advanced Technology Microwave Sounder observations developed to retrieve high-latitude snowfall events. The main novelty of the approach is the radiometric characterization of the background surface at the time of the overpass, which is ancillary to the retrieval process. The algorithm shows a unique capability to retrieve snowfall in the environmental conditions typical of high latitudes.
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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In this paper we evaluate the performance of two estimates of the global horizontal irradiance (GHI), one derived from the Meteosat Second Generation and one from a meteorological model (Regional Atmospheric Modeling System) forecast. The focus area is Italy, and the performance is evaluated for 12 pyranometers spanning a range of climate conditions, from Mediterranean maritime to Alpine.
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
Short summary
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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
Short summary
Short summary
The CCA algorithm is applicable to any modern passive microwave radiometer on board polar orbiting satellites; it has been developed using a data set of co-located SSMIS and TRMM-PR measurements and AMSU-MHS and TRMM-PR measurements. The algorithm shows a small rate of false alarms and superior detection capability and can efficiently detect (POD between 0.55 and 0.71) minimum rain rate varying from 0.14 mm/h (AMSU over ocean) to 0.41 (SSMIS over coast).
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
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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
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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.
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
Annalina Lombardi, Barbara Tomassetti, Valentina Colaiuda, Ludovico Di Antonio, Paolo Tuccella, Mario Montopoli, Giovanni Ravazzani, Frank Silvio Marzano, Raffaele Lidori, and Giulia Panegrossi
Hydrol. Earth Syst. Sci., 28, 3777–3797, https://doi.org/10.5194/hess-28-3777-2024, https://doi.org/10.5194/hess-28-3777-2024, 2024
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The accurate estimation of precipitation and its spatial variability within a watershed is crucial for reliable discharge simulations. The study is the first detailed analysis of the potential usage of the cellular automata technique to merge different rainfall data inputs to hydrological models. This work shows an improvement in the performance of hydrological simulations when satellite and rain gauge data are merged.
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi
Atmos. Meas. Tech., 17, 2195–2217, https://doi.org/10.5194/amt-17-2195-2024, https://doi.org/10.5194/amt-17-2195-2024, 2024
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The paper describes a new machine-learning-based snowfall retrieval algorithm for Advanced Technology Microwave Sounder observations developed to retrieve high-latitude snowfall events. The main novelty of the approach is the radiometric characterization of the background surface at the time of the overpass, which is ancillary to the retrieval process. The algorithm shows a unique capability to retrieve snowfall in the environmental conditions typical of high latitudes.
Rosa Claudia Torcasio, Alessandra Mascitelli, Eugenio Realini, Stefano Barindelli, Giulio Tagliaferro, Silvia Puca, Stefano Dietrich, and Stefano Federico
Nat. Hazards Earth Syst. Sci., 23, 3319–3336, https://doi.org/10.5194/nhess-23-3319-2023, https://doi.org/10.5194/nhess-23-3319-2023, 2023
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This work shows how local observations can improve precipitation forecasting for severe weather events. The improvement lasts for at least 6 h of forecast.
Monica Campanelli, Henri Diémoz, Anna Maria Siani, Alcide di Sarra, Anna Maria Iannarelli, Rei Kudo, Gabriele Fasano, Giampietro Casasanta, Luca Tofful, Marco Cacciani, Paolo Sanò, and Stefano Dietrich
Atmos. Meas. Tech., 15, 1171–1183, https://doi.org/10.5194/amt-15-1171-2022, https://doi.org/10.5194/amt-15-1171-2022, 2022
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The aerosol optical depth (AOD) characteristics in an urban area of Rome were retrieved over a period of 11 years (2010–2020) to determine, for the first time, their effect on the incoming ultraviolet (UV) solar radiation. The surface forcing efficiency shows that the AOD is the primary parameter affecting the surface irradiance in Rome, and it is found to be greater for smaller zenith angles and for larger and more absorbing particles in the UV range (such as, e.g., mineral dust).
Ayham Alyosef, Domenico Cimini, Lorenzo Luini, Carlo Riva, Frank S. Marzano, Marianna Biscarini, Luca Milani, Antonio Martellucci, Sabrina Gentile, Saverio T. Nilo, Francesco Di Paola, Ayman Alkhateeb, and Filomena Romano
Atmos. Meas. Tech., 14, 2737–2748, https://doi.org/10.5194/amt-14-2737-2021, https://doi.org/10.5194/amt-14-2737-2021, 2021
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Telecommunication is based on the propagation of radio signals through the atmosphere. The signal power diminishes along the path due to atmospheric attenuation, which needs to be estimated to be accounted for. In a study funded by the European Space Agency, we demonstrate an innovative method improving atmospheric attenuation estimates from ground-based radiometric measurements by 10–30 %. More accurate atmospheric attenuation estimates imply better telecommunication services in the future.
Stefano Federico, Rosa Claudia Torcasio, Elenio Avolio, Olivier Caumont, Mario Montopoli, Luca Baldini, Gianfranco Vulpiani, and Stefano Dietrich
Nat. Hazards Earth Syst. Sci., 19, 1839–1864, https://doi.org/10.5194/nhess-19-1839-2019, https://doi.org/10.5194/nhess-19-1839-2019, 2019
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This study shows the possibility to improve the weather forecast at the very short range (0–3 h) using lightning and/or radar reflectivity observations. We consider two challenging events that occurred over Italy, named Serrano and Livorno, characterized by moderate and exceptional rainfall, respectively.
The improvement given to the forecast by using the lightning and/or radar reflectivity observations is considerable. The best performance is obtained when using both data.
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
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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.
Monica Campanelli, Alessandra Mascitelli, Paolo Sanò, Henri Diémoz, Victor Estellés, Stefano Federico, Anna Maria Iannarelli, Francesca Fratarcangeli, Augusto Mazzoni, Eugenio Realini, Mattia Crespi, Olivier Bock, Jose A. Martínez-Lozano, and Stefano Dietrich
Atmos. Meas. Tech., 11, 81–94, https://doi.org/10.5194/amt-11-81-2018, https://doi.org/10.5194/amt-11-81-2018, 2018
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The estimation of precipitable water vapour (W) content is of great interest in both meteorological and climatological studies. Sun photometers allowed the development of W automatic estimations with high temporal resolution. A new methodology, based on the hypothesis that the calibration parameters characterizing the atmospheric transmittance are dependent on vertical profiles of temperature, air pressure and moisture typical of each measurement site, has been presented providing good results.
Stefano Federico, Marco Petracca, Giulia Panegrossi, Claudio Transerici, and Stefano Dietrich
Adv. Sci. Res., 14, 187–194, https://doi.org/10.5194/asr-14-187-2017, https://doi.org/10.5194/asr-14-187-2017, 2017
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This study investigates the impact of using lightning data on the precipitation forecast at different forecast ranges (3–24 h). Twenty case studies, occurred over Italy in fall 2012, are selected to show the impact.
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
Short summary
Short summary
In this paper we evaluate the performance of two estimates of the global horizontal irradiance (GHI), one derived from the Meteosat Second Generation and one from a meteorological model (Regional Atmospheric Modeling System) forecast. The focus area is Italy, and the performance is evaluated for 12 pyranometers spanning a range of climate conditions, from Mediterranean maritime to Alpine.
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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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
Short summary
Short summary
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
Short summary
Short summary
The CCA algorithm is applicable to any modern passive microwave radiometer on board polar orbiting satellites; it has been developed using a data set of co-located SSMIS and TRMM-PR measurements and AMSU-MHS and TRMM-PR measurements. The algorithm shows a small rate of false alarms and superior detection capability and can efficiently detect (POD between 0.55 and 0.71) minimum rain rate varying from 0.14 mm/h (AMSU over ocean) to 0.41 (SSMIS over coast).
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
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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
Short summary
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
Related subject area
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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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.
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.
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.
Laura M. Tomkins, Sandra E. Yuter, and Matthew A. Miller
Atmos. Meas. Tech., 17, 3377–3399, https://doi.org/10.5194/amt-17-3377-2024, https://doi.org/10.5194/amt-17-3377-2024, 2024
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We have created a new method to better identify enhanced features in radar data from winter storms. Unlike the clear-cut features seen in warm-season storms, features in winter storms are often fuzzier with softer edges. Our technique is unique because it uses two adaptive thresholds that change based on the background radar values. It can identify both strong and subtle features in the radar data and takes into account uncertainties in the detection process.
Eileen Päschke and Carola Detring
Atmos. Meas. Tech., 17, 3187–3217, https://doi.org/10.5194/amt-17-3187-2024, https://doi.org/10.5194/amt-17-3187-2024, 2024
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Little noise in radial velocity Doppler lidar measurements can contribute to large errors in retrieved turbulence variables. In order to distinguish between plausible and erroneous measurements we developed new filter techniques that work independently of the choice of a specific threshold for the signal-to-noise ratio. The performance of these techniques is discussed both by means of assessing the filter results and by comparing retrieved turbulence variables versus independent measurements.
Luuk D. van der Valk, Miriam Coenders-Gerrits, Rolf W. Hut, Aart Overeem, Bas Walraven, and Remko Uijlenhoet
Atmos. Meas. Tech., 17, 2811–2832, https://doi.org/10.5194/amt-17-2811-2024, https://doi.org/10.5194/amt-17-2811-2024, 2024
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Microwave links, often part of mobile phone networks, can be used to measure rainfall along the link path by determining the signal loss caused by rainfall. We use high-frequency data of multiple microwave links to recreate commonly used sampling strategies. For time intervals up to 1 min, the influence of sampling strategies on estimated rainfall intensities is relatively little, while for intervals longer than 5–15 min, the sampling strategy can have significant influences on the estimates.
Martin Lainer, Killian P. Brennan, Alessandro Hering, Jérôme Kopp, Samuel Monhart, Daniel Wolfensberger, and Urs Germann
Atmos. Meas. Tech., 17, 2539–2557, https://doi.org/10.5194/amt-17-2539-2024, https://doi.org/10.5194/amt-17-2539-2024, 2024
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This study uses deep learning (the Mask R-CNN model) on drone-based photogrammetric data of hail on the ground to estimate hail size distributions (HSDs). Traditional hail sensors' limited areas complicate the full HSD retrieval. The HSD of a supercell event on 20 June 2021 is retrieved and contains > 18 000 hailstones. The HSD is compared to automatic hail sensor measurements and those of weather-radar-based MESHS. Investigations into ground hail melting are performed by five drone flights.
Sebastian Rhode, Peter Preusse, Jörn Ungermann, Inna Polichtchouk, Kaoru Sato, Shingo Watanabe, Manfred Ern, Karlheinz Nogai, Björn-Martin Sinnhuber, and Martin Riese
EGUsphere, https://doi.org/10.5194/egusphere-2024-1084, https://doi.org/10.5194/egusphere-2024-1084, 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 the general atmospheric circulation and atmospheric processes.
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi
Atmos. Meas. Tech., 17, 2195–2217, https://doi.org/10.5194/amt-17-2195-2024, https://doi.org/10.5194/amt-17-2195-2024, 2024
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The paper describes a new machine-learning-based snowfall retrieval algorithm for Advanced Technology Microwave Sounder observations developed to retrieve high-latitude snowfall events. The main novelty of the approach is the radiometric characterization of the background surface at the time of the overpass, which is ancillary to the retrieval process. The algorithm shows a unique capability to retrieve snowfall in the environmental conditions typical of high latitudes.
Lusheng Liang, Wenying Su, Sergio Sejas, Zachary Eitzen, and Norman G. Loeb
Atmos. Meas. Tech., 17, 2147–2163, https://doi.org/10.5194/amt-17-2147-2024, https://doi.org/10.5194/amt-17-2147-2024, 2024
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This paper describes an updated process to obtain unfiltered radiation from CERES satellite instruments by incorporating the most recent developments in radiative transfer modeling and ancillary input datasets (e.g., realistic representation of land surface radiation and climatology of surface temperatures and aerosols) during the past 20 years. The resulting global mean of instantaneous SW and LW fluxes is changed by less than 0.5 W m−2 with regional differences as large as 2.0 W m−2.
Maximilian Graf, Andreas Wagner, Julius Polz, Llorenç Lliso, José Alberto Lahuerta, Harald Kunstmann, and Christian Chwala
Atmos. Meas. Tech., 17, 2165–2182, https://doi.org/10.5194/amt-17-2165-2024, https://doi.org/10.5194/amt-17-2165-2024, 2024
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Commercial microwave links (CMLs) can be used for rainfall retrieval. The detection of rainy periods in their attenuation time series is a crucial processing step. We investigate the usage of rainfall data from MSG SEVIRI for this task, compare this approach with existing methods, and introduce a novel combined approach. The results show certain advantages for SEVIRI-based methods, particularly for CMLs where existing methods perform poorly. Our novel combination yields the best performance.
Lieuwe G. Tilstra, Martin de Graaf, Victor J. H. Trees, Pavel Litvinov, Oleg Dubovik, and Piet Stammes
Atmos. Meas. Tech., 17, 2235–2256, https://doi.org/10.5194/amt-17-2235-2024, https://doi.org/10.5194/amt-17-2235-2024, 2024
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This paper introduces a new surface albedo climatology of directionally dependent Lambertian-equivalent reflectivity (DLER) observed by TROPOMI on the Sentinel-5 Precursor satellite. The database contains monthly fields of DLER for 21 wavelength bands at a relatively high spatial resolution of 0.125 by 0.125 degrees. The anisotropy of the surface reflection is handled by parameterisation of the viewing angle dependence.
Bing Cao and Alan Z. Liu
Atmos. Meas. Tech., 17, 2123–2146, https://doi.org/10.5194/amt-17-2123-2024, https://doi.org/10.5194/amt-17-2123-2024, 2024
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A narrow-band sodium lidar measures atmospheric waves but is limited to vertical variations. We propose to utilize phase shifts among observations from different laser beams to derive horizontal wave information. Two gravity wave packets were identified by this method. Both waves were found to interact with thin evanescent layers, partially reflected, but transmitted energy to higher altitudes. The method can detect more medium-frequency gravity waves for similar lidar systems worldwide.
Suyoung Sim, Sungwon Choi, Daeseong Jung, Jongho Woo, Nayeon Kim, Sungwoo Park, Honghee Kim, Ukkyo Jeong, Hyunkee Hong, and Kyung-Soo Han
EGUsphere, https://doi.org/10.5194/egusphere-2024-601, https://doi.org/10.5194/egusphere-2024-601, 2024
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Our study presents a novel method for satellite-based surface reflectance estimation, using the bi-directional Reflectance Distribution Function (BRDF) model to derive Background Surface Reflectance (BSR) in UV-VIS hyperspectral satellite imagery. Through comprehensive analysis, we show that BSR offers higher accuracy and greater stability compared to Lambertian Equivalent Reflectance (LER) methods. This data can offer a promising tool for accurate climate analysis and air quality monitoring.
Xiaozhen Xiong, Xu Liu, Robert Spurr, Ming Zhao, Qiguang Yang, Wan Wu, and Liqiao Lei
Atmos. Meas. Tech., 17, 1965–1978, https://doi.org/10.5194/amt-17-1965-2024, https://doi.org/10.5194/amt-17-1965-2024, 2024
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The term “hotspot” refers to the sharp increase in reflectance occurring when incident (solar) and reflected (viewing) directions coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications, but current models typically require large values of computations to represent the hotspot accurately. This paper provides a numerically improved hotspot BRDF model that converges much faster and is used in VLIDORT.
Daniel Zawada, Kimberlee Dubé, Taran Warnock, Adam Bourassa, Susann Tegtmeier, and Douglas Degenstein
Atmos. Meas. Tech., 17, 1995–2010, https://doi.org/10.5194/amt-17-1995-2024, https://doi.org/10.5194/amt-17-1995-2024, 2024
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There remain large uncertainties in long-term changes of stratospheric–atmospheric temperatures. We have produced a time series of more than 20 years of satellite-based temperature measurements from the OSIRIS instrument in the upper–middle stratosphere. The dataset is publicly available and intended to be used for a better understanding of changes in stratospheric temperatures.
Witali Krochin, Axel Murk, and Gunter Stober
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-42, https://doi.org/10.5194/amt-2024-42, 2024
Revised manuscript accepted for AMT
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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 manuscript, 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 20–50 km at the geographical location of Payerne (Switzerland).
Alban Philibert, Marie Lothon, Julien Amestoy, Pierre-Yves Meslin, Solène Derrien, Yannick Bezombes, Bernard Campistron, Fabienne Lohou, Antoine Vial, Guylaine Canut-Rocafort, Joachim Reuder, and Jennifer K. Brooke
Atmos. Meas. Tech., 17, 1679–1701, https://doi.org/10.5194/amt-17-1679-2024, https://doi.org/10.5194/amt-17-1679-2024, 2024
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We present a new algorithm, CALOTRITON, for the retrieval of the convective boundary layer depth with ultra-high-frequency radar measurements. CALOTRITON is partly based on the principle that the top of the convective boundary layer is associated with an inversion and a decrease in turbulence. It is evaluated using ceilometer and radiosonde data. It is able to qualify the complexity of the vertical structure of the low troposphere and detect internal or residual layers.
Kamil Mroz, Alessandro Battaglia, and Ann M. Fridlind
Atmos. Meas. Tech., 17, 1577–1597, https://doi.org/10.5194/amt-17-1577-2024, https://doi.org/10.5194/amt-17-1577-2024, 2024
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In this study, we examine the extent to which radar measurements from space can inform us about the properties of clouds and precipitation. Surprisingly, our analysis showed that the amount of ice turning into rain was lower than expected in the current product. To improve on this, we came up with a new way to extract information about the size and concentration of particles from radar data. As long as we use this method in the right conditions, we can even estimate how dense the ice is.
Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum
EGUsphere, https://doi.org/10.5194/egusphere-2024-167, https://doi.org/10.5194/egusphere-2024-167, 2024
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The amount of sunlight reflected by Earth’s surface (albedo) is crucial for its radiative system. Satellite instruments offer detailed spatial and temporal albedo maps, but only in seven specific wavelength bands. We generate albedo maps that fully cover the visible and near-infrared range with a machine learning algorithm. These provide information about how the reflectivity of different land surfaces vary through the year. Our dataset enhances the understanding of Earth's energy balance.
Volker Wulfmeyer, Christoph Senff, Florian Späth, Andreas Behrendt, Diego Lange, Robert M. Banta, W. Alan Brewer, Andreas Wieser, and David D. Turner
Atmos. Meas. Tech., 17, 1175–1196, https://doi.org/10.5194/amt-17-1175-2024, https://doi.org/10.5194/amt-17-1175-2024, 2024
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A simultaneous deployment of Doppler, temperature, and water-vapor lidar systems is used to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of weather forecast and climate models.
Daisuke Hotta, Katrin Lonitz, and Sean Healy
Atmos. Meas. Tech., 17, 1075–1089, https://doi.org/10.5194/amt-17-1075-2024, https://doi.org/10.5194/amt-17-1075-2024, 2024
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Global Navigation Satellite System (GNSS) polarimetric radio occultation (PRO) is a new type of GNSS observations that can detect heavy precipitation along the ray path between the emitter and receiver satellites. As a first step towards using these observations in numerical weather prediction (NWP), we developed a computer code that simulates GNSS-PRO observations from forecast fields produced by an NWP model. The quality of the developed simulator is evaluated with a number of case studies.
Mohamed Mossad, Irina Strelnikova, Robin Wing, and Gerd Baumgarten
Atmos. Meas. Tech., 17, 783–799, https://doi.org/10.5194/amt-17-783-2024, https://doi.org/10.5194/amt-17-783-2024, 2024
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This numerical study addresses observational gaps' impact on atmospheric gravity wave spectra. Three methods, fast Fourier transform (FFT), generalized Lomb–Scargle periodogram (GLS), and Haar structure function (HSF), were tested on synthetic data. HSF is best for spectra with negative slopes. GLS excels for flat and positive slopes and identifying dominant frequencies. Accurately estimating these aspects is crucial for understanding gravity wave dynamics and energy transfer in the atmosphere.
Kuo-Nung Wang, Chi O. Ao, Mary G. Morris, George A. Hajj, Marcin J. Kurowski, Francis J. Turk, and Angelyn W. Moore
Atmos. Meas. Tech., 17, 583–599, https://doi.org/10.5194/amt-17-583-2024, https://doi.org/10.5194/amt-17-583-2024, 2024
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In this article, we described a joint retrieval approach combining two techniques, RO and MWR, to obtain high vertical resolution and solve for temperature and moisture independently. The results show that the complicated structure in the lower troposphere can be better resolved with much smaller biases, and the RO+MWR combination is the most stable scenario in our sensitivity analysis. This approach is also applied to real data (COSMIC-2/Suomi-NPP) to show the promise of joint RO+MWR retrieval.
Filippo Emilio Scarsi, Alessandro Battaglia, Frederic Tridon, Paolo Martire, Ranvir Dhillon, and Anthony Illingworth
Atmos. Meas. Tech., 17, 499–514, https://doi.org/10.5194/amt-17-499-2024, https://doi.org/10.5194/amt-17-499-2024, 2024
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The WIVERN mission, one of the two candidates to be the ESA's Earth Explorer 11 mission, aims at providing measurements of horizontal winds in cloud and precipitation systems through a conically scanning W-band Doppler radar. This work discusses four methods that can be used to characterize and correct the Doppler velocity error induced by the antenna mispointing. The proposed methodologies can be extended to other Doppler concepts featuring conically scanning or slant viewing Doppler systems.
Luis Ackermann, Joshua Soderholm, Alain Protat, Rhys Whitley, Lisa Ye, and Nina Ridder
Atmos. Meas. Tech., 17, 407–422, https://doi.org/10.5194/amt-17-407-2024, https://doi.org/10.5194/amt-17-407-2024, 2024
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The paper addresses the crucial topic of hail damage quantification using radar observations. We propose a new radar-derived hail product that utilizes a large dataset of insurance hail damage claims and radar observations. A deep neural network was employed, trained with local meteorological variables and the radar observations, to better quantify hail damage. Key meteorological variables were identified to have the most predictive capability in this regard.
Christos Gatidis, Marc Schleiss, and Christine Unal
Atmos. Meas. Tech., 17, 235–245, https://doi.org/10.5194/amt-17-235-2024, https://doi.org/10.5194/amt-17-235-2024, 2024
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A common method to retrieve important information about the microphysical structure of rain (DSD retrievals) requires a constrained relationship between the drop size distribution parameters. The most widely accepted empirical relationship is between μ and Λ. The relationship shows variability across the different types of rainfall (convective or stratiform). The new proposed power-law model to represent the μ–Λ relation provides a better physical interpretation of the relationship coefficients.
Liqin Jin, Jakob Mann, Nikolas Angelou, and Mikael Sjöholm
Atmos. Meas. Tech., 16, 6007–6023, https://doi.org/10.5194/amt-16-6007-2023, https://doi.org/10.5194/amt-16-6007-2023, 2023
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By sampling the spectra from continuous-wave Doppler lidars very fast, the rain-induced Doppler signal can be suppressed and the bias in the wind velocity estimation can be reduced. The method normalizes 3 kHz spectra by their peak values before averaging them down to 50 Hz. Over 3 h, we observe a significant reduction in the bias of the lidar data relative to the reference sonic data when the largest lidar focus distance is used. The more it rains, the more the bias is reduced.
Florian Günzkofer, Gunter Stober, Dimitry Pokhotelov, Yasunobu Miyoshi, and Claudia Borries
Atmos. Meas. Tech., 16, 5897–5907, https://doi.org/10.5194/amt-16-5897-2023, https://doi.org/10.5194/amt-16-5897-2023, 2023
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Electric currents in the ionosphere can impact both satellite and ground-based infrastructure. These currents depend strongly on the collisions of ions and neutral particles. Measuring ion–neutral collisions is often only possible via certain assumptions. The direct measurement of ion–neutral collision frequencies is possible with multifrequency incoherent scatter radar measurements. This paper presents one analysis method of such measurements and discusses its advantages and disadvantages.
Neranga K. Hannadige, Peng-Wang Zhai, Meng Gao, Yongxiang Hu, P. Jeremy Werdell, Kirk Knobelspiesse, and Brian Cairns
Atmos. Meas. Tech., 16, 5749–5770, https://doi.org/10.5194/amt-16-5749-2023, https://doi.org/10.5194/amt-16-5749-2023, 2023
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We evaluated the impact of three ocean optical models with different numbers of free parameters on the performance of an aerosol and ocean color remote sensing algorithm using the multi-angle polarimeter (MAP) measurements. It was demonstrated that the three- and seven-parameter bio-optical models can be used to accurately represent both open and coastal waters, whereas the one-parameter model has smaller retrieval uncertainty over open water.
Konstantin Ntokas, Jörn Ungermann, Martin Kaufmann, Tom Neubert, and Martin Riese
Atmos. Meas. Tech., 16, 5681–5696, https://doi.org/10.5194/amt-16-5681-2023, https://doi.org/10.5194/amt-16-5681-2023, 2023
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A nanosatellite was developed to obtain 1-D vertical temperature profiles in the mesosphere and lower thermosphere, which can be used to derive wave parameters needed for atmospheric models. A new processing method is shown, which allows one to extract two 1-D temperature profiles. The location of the two profiles is analyzed, as it is needed for deriving wave parameters. We show that this method is feasible, which however will increase the requirements of an accurate calibration and processing.
Daniel Durbin, Yadong Wang, and Pao-Liang Chang
EGUsphere, https://doi.org/10.5194/egusphere-2023-2220, https://doi.org/10.5194/egusphere-2023-2220, 2023
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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 which uses swarm intelligence to seek the most suitable DSD which would produce the input measures.
Maya García-Comas, Bernd Funke, Manuel López-Puertas, Norbert Glatthor, Udo Grabowski, Sylvia Kellmann, Michael Kiefer, Andrea Linden, Belén Martínez-Mondéjar, Gabriele P. Stiller, and Thomas von Clarmann
Atmos. Meas. Tech., 16, 5357–5386, https://doi.org/10.5194/amt-16-5357-2023, https://doi.org/10.5194/amt-16-5357-2023, 2023
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We have released version 8 of MIPAS IMK–IAA temperatures and pointing information retrieved from MIPAS Middle and Upper Atmosphere mode version 8.03 calibrated spectra, covering 20–115 km altitude. We considered non-local thermodynamic equilibrium emission explicitly for each limb scan, essential to retrieve accurate temperatures above the mid-mesosphere. Comparisons of this temperature dataset with SABER measurements show excellent agreement, improving those of previous MIPAS versions.
Josef Innerkofler, Gottfried Kirchengast, Marc Schwärz, Christian Marquardt, and Yago Andres
Atmos. Meas. Tech., 16, 5217–5247, https://doi.org/10.5194/amt-16-5217-2023, https://doi.org/10.5194/amt-16-5217-2023, 2023
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Atmosphere remote sensing using GNSS radio occultation provides a highly valuable basis for atmospheric and climate science. For the highest-quality demands, the Wegener Center set up a rigorous system for processing low-level measurement data. This excess-phase processing setup includes integrated quality control and uncertainty estimation. It was successfully evaluated and inter-compared, ensuring the capability of producing reliable long-term data records for climate applications.
Jingna Bai, Yidong Lou, Weixing Zhang, Yaozong Zhou, Zhenyi Zhang, Chuang Shi, and Jingnan Liu
Atmos. Meas. Tech., 16, 5249–5259, https://doi.org/10.5194/amt-16-5249-2023, https://doi.org/10.5194/amt-16-5249-2023, 2023
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Homogenized atmospheric water vapor data are an important prerequisite for climate analysis. Compared to other techniques, GPS has an inherent homogeneity advantage but requires reprocessing and homogenization to eliminate impacts of applied strategy and observation environmental changes. The low-elevation cut-off angles are suggested for the best estimates of zenith tropospheric delay (ZTD) reprocessing time series when compared to homogenized radiosonde data or ERA5 reference time series.
James Barry, Stefanie Meilinger, Klaus Pfeilsticker, Anna Herman-Czezuch, Nicola Kimiaie, Christopher Schirrmeister, Rone Yousif, Tina Buchmann, Johannes Grabenstein, Hartwig Deneke, Jonas Witthuhn, Claudia Emde, Felix Gödde, Bernhard Mayer, Leonhard Scheck, Marion Schroedter-Homscheidt, Philipp Hofbauer, and Matthias Struck
Atmos. Meas. Tech., 16, 4975–5007, https://doi.org/10.5194/amt-16-4975-2023, https://doi.org/10.5194/amt-16-4975-2023, 2023
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Measured power data from solar photovoltaic (PV) systems contain information about the state of the atmosphere. In this work, power data from PV systems in the Allgäu region in Germany were used to determine the solar irradiance at each location, using state-of-the-art simulation and modelling. The results were validated using concurrent measurements of the incoming solar radiation in each case. If applied on a wider scale, this algorithm could help improve weather and climate models.
Wan Wu, Xu Liu, Liqiao Lei, Xiaozhen Xiong, Qiguang Yang, Qing Yue, Daniel K. Zhou, and Allen M. Larar
Atmos. Meas. Tech., 16, 4807–4832, https://doi.org/10.5194/amt-16-4807-2023, https://doi.org/10.5194/amt-16-4807-2023, 2023
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We present a new operational physical retrieval algorithm that is used to retrieve atmospheric properties for each single field-of-view measurement of hyper-spectral IR sounders. The physical scheme includes a cloud-scattering calculation in its forward-simulation part. The data product generated using this algorithm has an advantage over traditional IR sounder data production algorithms in terms of improved spatial resolution and minimized error due to cloud contamination.
Zhen Li, Ad Stoffelen, Anton Verhoef, Zhixiong Wang, Jian Shang, and Honggang Yin
Atmos. Meas. Tech., 16, 4769–4783, https://doi.org/10.5194/amt-16-4769-2023, https://doi.org/10.5194/amt-16-4769-2023, 2023
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WindRAD (Wind Radar) is the first dual-frequency rotating fan-beam scatterometer in orbit. We observe non-linearity in the backscatter distribution. Therefore, higher-order calibration (HOC) is proposed, which removes the non-linearities per incidence angle. The combination of HOC and NOCant is discussed. It can remove not only the non-linearity but also the anomalous harmonic azimuth dependencies caused by the antenna rotation; hence the optimal winds can be achieved with this combination.
Marco Gabella, Martin Lainer, Daniel Wolfensberger, and Jacopo Grazioli
Atmos. Meas. Tech., 16, 4409–4422, https://doi.org/10.5194/amt-16-4409-2023, https://doi.org/10.5194/amt-16-4409-2023, 2023
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A still wind turbine observed with a fixed-pointing radar antenna has shown distinctive polarimetric signatures: the correlation coefficient between the two orthogonal polarization states was persistently equal to 1. The differential reflectivity and the radar reflectivity factors were also stable in time. Over 2 min (2000 Hz, 128 pulses were used; consequently, the sampling time was 64 ms), the standard deviation of the differential backscattering phase shift was only a few degrees.
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...