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Dust Detection and Optical Depth Retrieval Using MSG‑SEVIRI Data

Filomena Romano, Elisabetta Ricciardelli, Domenico Cimini, Francesco Di Paola, Mariassunta Viggiano
2013 Atmosphere  
Thanks to its observational frequency of 15 min, the Meteosat Second Generation (MSG) geostationary satellite offers a great potential to monitor dust storms. To explore this potential, an algorithm for the detection and the retrieval of dust aerosol optical properties has been tested. This is a multispectral algorithm based on visible and infrared data which has been applied to 15 case studies selected between 2007 and 2011. The algorithm has been validated in the latitude-longitude box
more » ... 30 and 50 degrees north, and −10 and 20 degrees east, respectively. Hereafter we present the obtained results that have been validated against Aerosol Robotic Network (AERONET) ground-based measurements and compared with the retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua satellites. The dust aerosol optical depth variations observed at the AERONET sites are well reproduced, showing good correlation of about 0.77, and a root mean square difference within 0.08, and the spatial patterns retrieved by using the algorithm developed are in agreement with those observed by MODIS.
doi:10.3390/atmos4010035 fatcat:syorsszfofcvlahm6ubhtjlwvi

The Role of Emissivity in the Detection of Arctic Night Clouds

Filomena Romano, Domenico Cimini, Saverio Nilo, Francesco Di Paola, Elisabetta Ricciardelli, Ermann Ripepi, Mariassunta Viggiano
2017 Remote Sensing  
Nilo , Francesco Di Paola , Elisabetta Ricciardelli, Ermann Ripepi and Mariassunta Viggiano contributed to data processing, analysis and validation process.  ... 
doi:10.3390/rs9050406 fatcat:o557llluvre7dbbndq4noasmka

Downscaling of Satellite OPEMW Surface Rain Intensity Data

Angela Cersosimo, Salvatore Larosa, Filomena Romano, Domenico Cimini, Francesco Di Paola, Donatello Gallucci, Sabrina Gentile, Edoardo Geraldi, Saverio Teodosio Nilo, Elisabetta Ricciardelli, Ermann Ripepi, Mariassunta Viggiano
2018 Remote Sensing  
This paper presents a geostatistical downscaling procedure to improve the spatial resolution of precipitation data. The kriging method with external drift has been applied to surface rain intensity (SRI) data obtained through the Operative Precipitation Estimation at Microwave Frequencies (OPEMW), which is an algorithm for rain rate retrieval based on Advanced Microwave Sounding Units (AMSU) and Microwave Humidity Sounder (MHS) observations. SRI data have been downscaled from coarse initial
more » ... lution of AMSU-B/MHS radiometers to the fine resolution of Spinning Enhanced Visible and InfraRed Imager (SEVIRI) flying on board the Meteosat Second Generation (MSG) satellite. Orographic variables, such as slope, aspect and elevation, are used as auxiliary data in kriging with external drift, together with observations from Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager (MSG-SEVIRI) in the water vapor band (6.2 µm and 7.3 µm) and in thermal-infrared (10.8 µm and 8.7 µm). The validation is performed against measurements from a network of ground-based rain gauges in Southern Italy. It is shown that the approach provides higher accuracy with respect to ordinary kriging, given a choice of auxiliary variables that depends on precipitation type, here classified as convective or stratiform. Mean values of correlation (0.52), bias (0.91 mm/h) and root mean square error (2.38 mm/h) demonstrate an improvement by +13%, −37%, and −8%, respectively, for estimates derived by kriging with external drift with respect to the ordinary kriging.
doi:10.3390/rs10111763 fatcat:r2idie2vlfbi7ejx4dfyvkz3dq

Fog Forecast Using WRF Model Output for Solar Energy Applications

Saverio Teodosio Nilo, Domenico Cimini, Francesco Di Paola, Donatello Gallucci, Sabrina Gentile, Edoardo Geraldi, Salvatore Larosa, Elisabetta Ricciardelli, Ermann Ripepi, Mariassunta Viggiano, Filomena Romano
2020 Energies  
(Elisabetta Ricciardelli) and E.R. (Ermann Ripepi); formal analysis, S.T.N., D.C., D.G., S.G. and E.R. (Elisabetta Ricciardelli); funding acquisition, E.G.; investigation, E.R.  ...  (Elisabetta Ricciardelli); Methodology, S.T.N.; project administration, E.G. and F.R.; resources, S.G., E.R.  ... 
doi:10.3390/en13226140 fatcat:dcgmoxjooba6jil6xbc3gcpey4

Improvement in Surface Solar Irradiance Estimation Using HRV/MSG Data

Filomena Romano, Domenico Cimini, Angela Cersosimo, Francesco Di Paola, Donatello Gallucci, Sabrina Gentile, Edoardo Geraldi, Salvatore Larosa, Saverio T. Nilo, Elisabetta Ricciardelli, Ermann Ripepi, Mariassunta Viggiano
2018 Remote Sensing  
(Elisabetta Ricciardelli) and E.G., designed the research, wrote the paper and contributed to evaluation process. D.G., F.D.P., S.G., A.C., S.T.N., E.R.  ... 
doi:10.3390/rs10081288 fatcat:4y3dtieftfc6zflsvlgxlfawdm

Analysis of Catania Flash Flood Case Study by Using Combined Microwave and Infrared Technique

Francesco Di Paola, Elisabetta Ricciardelli, Domenico Cimini, Filomena Romano, Mariassunta Viggiano, Vincenzo Cuomo
2014 Journal of Hydrometeorology  
In this paper, the analysis of an extreme convective event atypical for the winter season, which occurred on 21 February 2013 on the east coast of Sicily and caused a flash flood over Catania, is presented. In just 1 h, more than 50 mm of precipitation was recorded, but it was not forecast by numerical weather prediction (NWP) models and, consequently, no severe weather warnings were sent to the population. The case study proposed is first examined with respect to the synoptic situation and
more » ... analyzed by means of two algorithms based on satellite observations: the Cloud Mask Coupling of Statistical and Physical Methods (MACSP) and the Precipitation Evolving Technique (PET), developed at the National Research Council of Italy. Both of the algorithms show their ability in the near-real-time monitoring of convective cell formation and their rapid evolution. As quantitative precipitation forecasts by NWP could fail, especially for atypical convective events like in Catania, tools like MACSP and PET shall be adopted by civil protection centers to monitor the realtime evolution of deep convection events in aid to the severe weather warning service.
doi:10.1175/jhm-d-13-092.1 fatcat:nahqt3d7wfh4ddwa4ndxvtbzia

Improvement of Hourly Surface Solar Irradiance Estimation Using MSG Rapid Scanning Service

Donatello Gallucci, Filomena Romano, Domenico Cimini, Francesco Di Paola, Sabrina Gentile, Salvatore Larosa, Saverio T. Nilo, Elisabetta Ricciardelli, Ermann Ripepi, Mariassunta Viggiano, Edoardo Geraldi
2019 Remote Sensing  
(Elisabetta Ricciardelli), S.L. and M.V. contributed to data processing. All the co-authors helped to revise the manuscript.  ... 
doi:10.3390/rs11010066 fatcat:6ge76obeh5acdcum7gz72gyfr4

Convective Initiation Proxies for Nowcasting Precipitation Severity Using the MSG-SEVIRI Rapid Scan

Donatello Gallucci, Maria Pia De Natale, Domenico Cimini, Francesco Di Paola, Sabrina Gentile, Edoardo Geraldi, Salvatore Larosa, Saverio Teodosio Nilo, Elisabetta Ricciardelli, Mariassunta Viggiano, Filomena Romano
2020 Remote Sensing  
In this study, we investigate the ability of several convective initiation predictors based on satellite infrared observations to distinguish convective weak precipitation events from those leading to intense rainfall. The two types of precipitation are identified according to hourly rainfall, respectively less than 10 mm and greater than 30 mm. The analysis is conducted on a representative dataset containing 92 severe and weak precipitation events collected over the Italian peninsula in the
more » ... iod 2016–2019 over June-September. The events are selected to be short-lived (i.e., less than 12 h) and localized (i.e., less than 50×50km2). Italian National Radar Network products, namely the Vertical Maximum Intensity (VMI) and the Surface Rain Total (SRT) variables (from Dewetra Platform by CIMA, Italian Civil Protection Department), are used as indicators of convection (i.e., VMI greater than 35 dBZ echo intensity) and cumulated rainfall, respectively. The considered predictors are linear combinations of spectral infrared channels measured with the Rapid Scan Service (RSS) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation (MSG) geostationary satellites. We select a 5×5 SEVIRI pixel-box centered on the storm core and perform a statistical analysis of the predictors up to 2.5 h around the event occurrence. We demonstrate that some of the proxies—describing growth and glaciation storm properties—show few degrees contrast between severe and nonsevere precipitation cases, hence carrying significant information to help discriminate the two types. We design a threshold scheme based on the three most informative predictors to distinguish weak and strong precipitation events. This analysis yields accuracy higher than 0.6 and the probability of false detection lower than 0.26; in terms of reducing false alarms, this method shows slight better performances compared to related works, at the expense of a lower probability of detection. The overall results, however, show limited capability for these infrared proxies as stand-alone predictors to distinguish severe from nonsevere precipitation events. Nonetheless, these may serve as additional tools to reduce the false alarm ratio in nowcasting algorithms for convective orographic storms. This study also provides further insight into the correlation between early infrared fields signatures prior to convection and subsequent evolution of the storms, extending previous works in this field.
doi:10.3390/rs12162562 fatcat:hxocknwxnjdtrbjtvykwo2asom

Nowcasting Surface Solar Irradiance with AMESIS via Motion Vector Fields of MSG-SEVIRI Data

Donatello Gallucci, Filomena Romano, Angela Cersosimo, Domenico Cimini, Francesco Di Paola, Sabrina Gentile, Edoardo Geraldi, Salvatore Larosa, Saverio Nilo, Elisabetta Ricciardelli, Mariassunta Viggiano
2018 Remote Sensing  
In this study, we compare different nowcasting techniques based upon the calculation of motion vector fields derived from spectral channels of Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager (MSG-SEVIRI). The outputs of the nowcasting techniques are used as inputs to the Advanced Model for Estimation of Surface solar Irradiance from Satellite (AMESIS), for predicting surface solar irradiance up to 2 h in advance. In particular, the first part of the methodology consists
more » ... in projecting the time evolution of each MSG-SEVIRI channel (for every pixel in the spatial domain) through extrapolation of a displacement vector field obtained by matching similar patterns within two successive MSG-SEVIRI data images. Different ways to implement the above method result in substantial differences in the predicted trajectory, leading to different performances depending on the time interval of interest. All the nowcasting techniques considered here systematically outperform the simple persistence method for all MSG-SEVIRI channels and for each case study used in this work; importantly, this occurs across the entire 2 h period of the forecast. In the second part of the algorithm, the predicted irradiance maps computed with AMESIS from the forecasted radiances, are shown to be in good agreement with irradiances derived from MSG measured radiances and improve on numerical weather model predictions, thus providing a feasible alternative for nowcasting surface solar radiation. The results show that the mean values for correlation, bias, and root mean square error vary across the time interval, ranging between 0.94, −1 W/m 2 , 61 W/m 2 after 15 min, and 0.73, −18 W/m 2 , 147 W/m 2 after 2 h, respectively. Remote Sens. 2018, 10, 845 2 of 17 great reduction to power supply [5] . Hence it is crucial to accurately monitor and forecast the position and trajectory of cloudy systems on very short-time scales. In the scientific literature this is referred to as nowcasting, i.e., short-term forecasting up to a few hours ahead (typically 0-2 h). Many approaches for nowcasting have been proposed, which can be generally classified as statistical or physical/deterministic. Statistical techniques rely on a training process based on past datasets, and learn how to infer the evolution of weather parameters through identification of historical patterns. The simplest of these approaches is the persistence method, for which the current status is projected as is to the future; other statistical techniques include, but are not limited to, multivariate regression and neural network [13] [14] [15] . Among the physical approaches, cloud tracking techniques are applied by processing images from geostationary satellites [5, [16] [17] [18] , total sky imagery [19] or using other ground sensors [20] . In particular, satellite-based methods allow for global coverage, and high quality images are nearly continuously available. Satellite imagery is currently exploited to derive Atmospheric Motion Vectors [21] (AMVs, i.e., wind vector) as well as Cloud Motion Vectors (CMVs) [22] [23] [24] ; these are obtained by analysing successive satellite images searching for the same features and to extrapolate the future trajectory on the basis of the recent past motion. The feature matching among subsequent images is performed by maximizing a pre-determined measure of similarity, which is typically either a correlation coefficient or the inverse of a mean square error. This technique was first implemented in [5] using a cross-correlation coefficient, similarly to [16] , showing an improvement over the simple persistence method. In [17] a cloud-tracking technique is applied to Meteosat images using a probabilistic prediction for the cloud cover; many improvements have been proposed since then [18, [25] [26] [27] . In [28,29] a variational method is used to minimise an energy like objective function incorporating the relevant features of the images analysed, while in [30] a disparity vector field for each pixel is used to perform the forecast. A huge body of literature can also be found on similar approaches, referred to as optical flow methods (see pioneer works [31, 32] ), which are typically applied in computer vision techniques; in practice these methods consist in the estimation of the distribution of brightness patterns of an image. A comprehensive description and analysis of these methods can be found in [4] , in which an hybrid approach combining block-matching methods [16, 33, 34] and variational optical flow is implemented. In this work, we exploit observations from Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite. We perform nowcasting of MSG-SEVIRI IR/VIS channels up to 2 h, by deriving motion vector fields in analogy to optical flow methods and cloud motion vector techniques. The forecasted radiances are then used as inputs to the Advanced Model for Estimation of Surface solar Irradiance from Satellite (AMESIS) [35] to predict surface solar irradiance maps. We extrapolate the motion with three different methods, compute the forecasted irradiance with AMESIS (using the forecasted radiances as input), and compare against the observed irradiance, derived with AMESIS from MSG measured radiances. To evaluate the degree of accuracy, we also include in the comparison the irradiance obtained with benchmarking methods such as simple persistence and the Weather Research and Forecasting (WRF) model [36] (see Appendix A for further details on the implementation of the model in this context). We emphasize that in this work we directly forecast MSG-SEVIRI IR/VIS radiances, while previous implemented satellite-based methods [37-41] generally pre-process the satellite images (e.g., to derive cloud index or cloud optical thickness maps), which then undergo an advection process to forecast the future position of cloud patterns. To summarise, the rationale for this work is twofold: i) to investigate and compare the performances of three variants of cloud motion technique, applied directly to the radiances measured by MSG-SEVIRI channels and ii) to define a self-consistent methodology providing nowcasting (up to 2 h) of surface solar irradiance maps, based on the integration of advection techniques and AMESIS. The paper proceeds as follows: in Section 2 we present the methodology adopted in this work, by firstly (Section 2.1) analysing and comparing three variants of the cloud motion technique (used here to advect the radiance values of MSG-SEVIRI channels) and secondly (Section 2.2) by describing the AMESIS model used here to nowcast irradiance. In Section 3 we compare the statistical performances
doi:10.3390/rs10060845 fatcat:nns6xfgmurgn3nbvwfiyzgp5ci

Analysis of Livorno Heavy Rainfall Event: Examples of Satellite-Based Observation Techniques in Support of Numerical Weather Prediction

Elisabetta Ricciardelli, Francesco Di Paola, Sabrina Gentile, Angela Cersosimo, Domenico Cimini, Donatello Gallucci, Edoardo Geraldi, Salvatore Larosa, Saverio Nilo, Ermann Ripepi, Filomena Romano, Mariassunta Viggiano
2018 Remote Sensing  
This study investigates the value of satellite-based observational algorithms in supporting numerical weather prediction (NWP) for improving the alert and monitoring of extreme rainfall events. To this aim, the analysis of the very intense precipitation that affected the city of Livorno on 9 and 10 September 2017 is performed by applying three remote sensing techniques based on satellite observations at infrared/visible and microwave frequencies and by using maps of accumulated rainfall from
more » ... weather research and forecasting (WRF) model. The satellite-based observational algorithms are the precipitation evolving technique (PET), the rain class evaluation from infrared and visible observations (RainCEIV) technique and the cloud classification mask coupling of statistical and physics methods (C-MACSP). Moreover, the rain rates estimated by the Italian Weather Radar Network are also considered to get a quantitative evaluation of RainCEIV and PET performance. The statistical assessment shows good skills for both the algorithms (for PET: bias = 1.03, POD = 0.76, FAR = 0.26; for RainCEIV: bias = 1.33, POD = 0.77, FAR = 0.41). In addition, a qualitative comparison among the three technique outputs, rain rate radar maps, and WRF accumulated rainfall maps is also carried out in order to highlight the advantages of the different techniques in providing real-time monitoring, as well as quantitative characterization of rainy areas, especially when rain rate measurements from Weather Radar Network and/or from rain gauges are not available.
doi:10.3390/rs10101549 fatcat:qtyonttvcrcp5fbqxfu6kotozm

Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel

Saverio Nilo, Filomena Romano, Jan Cermak, Domenico Cimini, Elisabetta Ricciardelli, Angela Cersosimo, Francesco Di Paola, Donatello Gallucci, Sabrina Gentile, Edoardo Geraldi, Salvatore Larosa, Ermann Ripepi (+1 others)
2018 Remote Sensing  
Author Contributions: Saverio Teodosio Nilo, Filomena Romano, Elisabetta Ricciardelli and Domenico Cimini designed the research, wrote the paper and contributed to evaluation process.  ... 
doi:10.3390/rs10040541 fatcat:rb6q5ax2i5gpxchvhbg4ymsmty

A high-resolution, integrated system for rice yield forecasting at district level

Valentina Pagani, Tommaso Guarneri, Lorenzo Busetto, Luigi Ranghetti, Mirco Boschetti, Ermes Movedi, Manuel Campos-Taberner, Francisco Javier Garcia-Haro, Dimitrios Katsantonis, Dimitris Stavrakoudis, Elisabetta Ricciardelli, Filomena Romano (+5 others)
2018 Agricultural Systems  
25 To meet the growing demand from public and private stakeholders for early yield estimates, a high-26 resolution (2 km × 2 km) rice yield forecasting system based on the integration between the WARM 27 model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped 28 area and to derive spatially distributed sowing dates, as well as for the dynamic assimilation of RS-29 derived leaf area index (LAI) data within the crop model. The systemtested for the main
more » ... an 30 rice production districts in Italy, Greece and Spainperformed satisfactorily: more than 66% of inter-31 annual yield variability was explained in six out of eight combinations of ecotype × district, with a 32 maximum of 89% of variability explained for Tropical Japonica cultivars in the Vercelli district 33 (Italy). In seven out of eight cases, the assimilation of RS-derived LAI allowed improving the 34 forecasting capability, with minor differences due to the assimilation technology used (updating or 35 recalibration). In particular, RS allowed reducing the uncertainty by capturing factors not properly 36 reproduced by the simulation model (given the uncertainty due to large-area simulations). As an 37 example, the season 2003 in the Serres (Greece) district was characterized by severe blast epidemics, 38 whose effect on canopy vigor was captured by RS-derived LAI products. The systemextending the 39 one used for rice within the EC-JRC-MARS forecasting systemwas pre-operationally used in 2015 40 and 2016 to provide early yield estimates to private companies and institutional stakeholders within 41 the EU-FP7 ERMES project. 42 43 Keywords 44 Assimilation; blast disease; Oryza sativa L.; remote sensing; WARM model. 45 46 as the need of defining selling strategies or planning milling operations (Everingham et al., 2002), the 52 interest of public institutions deals with the need of regulating agricultural markets and mitigating 53 volatility of prices in case of speculative actions on food commodities (e.g., OECD and FAO, 2011; 54 Kogan et al., 2013). Simple forecasting systemsbased, e.g., on agroclimatic indicators (Balaghi et 55 al., 2012)demonstrated their usefulness under conditions characterized by large year-to-year 56 fluctuations in yields and when those fluctuations are driven by one or two key factors. Other 57 approaches are more complex, relying on remote sensing (Mkhabela et al., 2005; Wang et al., 2010; 58 Duveiller et al., 2013; Son et al., 2014) or crop simulation models (Vossen and Rijks, 1995; Supit, 59 1997; Bezuidenhout and Singels, 2007a/b; de Wit et al., 2010; Kogan et al., 2013; Pagani et al., 2017). 60 Crop models are indeed able to interpret reality, e.g., by capturing the effects of weather anomalies or 61 other factors affecting crop yields better than simpler systems. As an example, they are able to 62 simulate the effect of thermal shocks-induced spikelet sterility (Shimono et al., 2005) . A forecasting 63 grown in the province of Vercellirepresenting about 15% of the national rice production. In Spain 132
doi:10.1016/j.agsy.2018.05.007 fatcat:4sn5llmlvjejvdg24apd3hbecu

MiRTaW: An Algorithm for Atmospheric Temperature and Water Vapor Profile Estimation from ATMS Measurements Using a Random Forests Technique

Francesco Di Paola, Elisabetta Ricciardelli, Domenico Cimini, Angela Cersosimo, Arianna Di Paola, Donatello Gallucci, Sabrina Gentile, Edoardo Geraldi, Salvatore Larosa, Saverio Nilo, Ermann Ripepi, Filomena Romano (+2 others)
2018 Remote Sensing  
A new algorithm for the estimation of atmospheric temperature (T) and water vapor (WV) vertical profiles in nonprecipitating conditions is presented. The microwave random forest temperature and water vapor (MiRTaW) profiling algorithm is based on the random forest (RF) technique and it uses microwave (MW) sounding from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. Three different data sources were chosen for both
more » ... and validation purposes, namely, the ERA-Interim from the European Centre for Medium-Range Weather Forecasts (ECMWF), the Infrared Atmospheric Sounding Interferometer Atmospheric Temperature Water Vapour and Surface Skin Temperature (IASI L2 v6) from the Meteorological Operational satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the radiosonde observations from the Integrated Global Radiosonde Archive (IGRA). The period from 2012 to 2016 was considered in the training dataset; particular attention was paid to the instance selection procedure, in order to reduce the full training dataset with negligible information loss. The out-of-bag (OOB) error was computed and used to select the optimal RF parameters. Different RFs were trained, one for each vertical level: 32 levels for T (within 10–1000 hPa) and 23 levels for WV (200–1000 hPa). The validation of the MiRTaW profiling algorithm was conducted on a dataset from 2017. The mean bias error (MBE) of T vertical profiles ranges within about (−0.4–0.4) K, while for the WV mixing ratio, the MBE starts at ~0.5 g/kg near the surface and decreases to ~0 g/kg at 200 hPa level, in line with the expectations.
doi:10.3390/rs10091398 fatcat:i5ut242pwjgpfkofiqokn5hnqi

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

Sabrina Gentile, Francesco Di Paola, Domenico Cimini, Donatello Gallucci, Edoardo Geraldi, Salvatore Larosa, Saverio T. Nilo, Elisabetta Ricciardelli, Ermann Ripepi, Mariassunta Viggiano, Filomena Romano
2020 Remote Sensing  
(Elisabetta Ricciardelli); Visualization, S.L.; Supervision, F.R.; Project Administration, E.G.; Funding Acquisition, D.C. All authors have read and agreed to the published version of the manuscript.  ... 
doi:10.3390/rs12060920 fatcat:y6yc2nkgyfb2fjxfzezlbezrzq

RTTOV-gb v1.0 – updates on sensors, absorption models, uncertainty, and availability

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 (+9 others)
2019 Geoscientific Model Development  
<p><strong>Abstract.</strong> This paper describes the first official release (v1.0) of RTTOV-gb. RTTOV-gb is a FORTRAN 90 code developed by adapting the atmospheric radiative transfer code RTTOV, focused on satellite-observing geometry, to the ground-based observing geometry. RTTOV-gb is designed to simulate ground-based upward-looking microwave radiometer (MWR) observations of atmospheric downwelling natural radiation in the frequency range from 22 to 150&amp;thinsp;GHz. Given an atmospheric
more » ... rofile of temperature, water vapor, and, optionally, cloud liquid water content, and together with a viewing geometry, RTTOV-gb computes downwelling radiances and brightness temperatures leaving the bottom of the atmosphere in each of the channels of the sensor being simulated. In addition, it provides the sensitivity of observations to the atmospheric thermodynamical state, i.e., the Jacobians. Therefore, RTTOV-gb represents the forward model needed to assimilate ground-based MWR data into numerical weather prediction models, which is currently pursued internationally by several weather services. RTTOV-gb is fully described in a previous paper (De Angelis et al., 2016), while several updates are described here. In particular, two new MWR types and a new parameterization for the atmospheric absorption model have been introduced since the first paper. In addition, estimates of the uncertainty associated with the absorption model and with the fast parameterization are given here. Brightness temperatures (<span class="inline-formula"><strong>T</strong><sub>B</sub></span>) computed with RTTOV-gb v1.0 from radiosonde profiles have been compared with ground-based MWR observations in six channels (23.8, 31.4, 72.5, 82.5, 90.0, and 150.0&amp;thinsp;GHz). The comparison shows statistics within the expected accuracy. RTTOV-gb is now available to licensed users free of charge from the Numerical Weather Prediction Satellite Application Facility (NWP SAF) website, after registration. Coefficients for four MWR instrument types and two absorption model parameterizations are also freely available from the RTTOV-gb support website.</p>
doi:10.5194/gmd-12-1833-2019 fatcat:rhvvqkf5a5aqnaxyly5rclvafm
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