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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 32312

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Dr. Dimitrios Katsanos

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Institute for Environmental Research and Sustainable Development (IERSD), National Observatory of Athens (NOA), National Observatory of Athens, Athens, Greece
Interests: precipitation science; remote sensing of precipitation; storm and atmospheric electrical activity; numerical weather forecasting models
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Dear Colleagues,

Satellite observation includes a wide variety of remotely-sensed meteorological parameters that are crucial in atmospheric modeling. During the past three decades, numerous satellite platforms have been used in order to estimate key parameters such as precipitation, temperature, pressure, wind, clouds, water vapor, and lightning. More recently, InSAR and GNSS data have been gaining ground, since they are considered a reliable source for the estimation of water vapor content in the atmosphere, through the calculation of the corresponding path delay that the troposphere introduces to satellite signals.

The assimilation of satellite data to numerical weather prediction (NWP) models is one of the most essential applications of such data in atmospheric modeling. The result of data assimilation is the improvement of the initial conditions of the models that can lead to an analogous improvement of weather forecasting. Using satellite observations of high accuracy and resolution can fill in the gaps over areas where in situ measurements are absent. Similarly, climate models can benefit from the use of reliable satellite databases. Towards that direction, data assimilation is a developing area of research that continues to explore the prospects of using all available meteorological parameters and pursues new methods of accomplishing it.

This Special Issue expects contributions on:

Numerical weather prediction using data assimilation of satellite observations (including InSAR and GNSS);
Data assimilation of remote sensing data in climate modeling;
Remote sensing of atmospheric parameters that can be assimilated to atmospheric models;
New methods of data assimilation.

Dr. Dimitrios Katsanos
Guest Editor

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Published Papers (10 papers)

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16 pages, 4730 KiB

Open AccessArticle

Tomographic Inversion Methods for Retrieving the Tropospheric Water Vapor Content Based on the NDSA Measurement Approach

by Agnese Mazzinghi, Fabrizio Cuccoli, Fabrizio Argenti, Arjan Feta and Luca Facheris

Remote Sens. 2022, 14(2), 414; https://doi.org/10.3390/rs14020414 - 17 Jan 2022

Cited by 2 | Viewed by 2049

Abstract

In this paper, we deal with the problem of retrieving maps of tropospheric Water Vapor (WV) concentration by means of a set of Low Earth Orbit (LEO) satellites orbiting in the same plane and along the same direction. It is assumed that a number of microwave links is deployed between a group of satellites with microwave transmitters onboard and another group with receivers. It is also assumed that the Normalized Differential Spectral Absorption (NDSA) approach is used to provide time series of Integrated Water Vapor (IWV) along each link. The set of links scans an annular region belonging to the orbital plane of the LEO satellites, so that the time series of the IWV measurements can be exploited to retrieve the WV concentration in such a region. This is a typical tomographic inversion problem. The geometry of the acquisition system and the path-integrated nature of measurements well fit the application of the Exterior Reconstruction Tomographic Algorithm (ERTA), which was proposed several decades ago in contexts different from remote sensing. In this paper, we investigate the potential of ERTA for the WV retrieval and compare its performance with that of a least square inversion approach already presented in the literature. The compared analysis has been carried out through simulations that have highlighted the peculiarities and retrieval capabilities of the two tomographic methods, as well as the impact of the richness of the satellite constellation on the overall performance. Full article

(This article belongs to the Special Issue Satellite Observation for Atmospheric Modeling)

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16 pages, 4575 KiB

Open AccessArticle

Evaluation of the Simulation of Typhoon Lekima (2019) Based on Different Physical Parameterization Schemes and FY-3D Satellite’s MWHS-2 Data Assimilation

by Dongmei Xu, Xuewei Zhang, Hong Li, Haiying Wu, Feifei Shen, Aiqing Shu, Yi Wang and Xiaoran Zhuang

Remote Sens. 2021, 13(22), 4556; https://doi.org/10.3390/rs13224556 - 12 Nov 2021

Cited by 9 | Viewed by 2594

Abstract

In this study, the case of super typhoon Lekima, which landed in Jiangsu and Zhejiang Province on 4 August 2019, is numerically simulated. Based on the Weather Research and Forecasting (WRF) model, the sensitivity experiments are carried out with different combinations of physical parameterization schemes. The results show that microphysical schemes have obvious impacts on the simulation of the typhoon’s track, while the intensity of the simulated typhoon is more sensitive to surface physical schemes. Based on the results of the typhoon’s track and intensity simulation, one parameterization scheme was further selected to provide the background field for the following data assimilation experiments. Using the three-dimensional variational (3DVar) data assimilation method, the Microwave Humidity Sounder-2 (MWHS-2) radiance data onboard the Fengyun-3D satellite (FY-3D) were assimilated for this case. It was found that the assimilation of the FY-3D MWHS-2 radiance data was able to optimize the initial field of the numerical model in terms of the model variables, especially for the humidity. Finally, by the inspection of the typhoon’s track and intensity forecast, it was found that the assimilation of FY-3D MWHS-2 radiance data improved the skill of the prediction for both the typhoon’s track and intensity. Full article

(This article belongs to the Special Issue Satellite Observation for Atmospheric Modeling)

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16 pages, 2801 KiB

Open AccessArticle

Retrieving Precipitable Water Vapor from Real-Time Precise Point Positioning Using VMF1/VMF3 Forecasting Products

by Peng Sun, Kefei Zhang, Suqin Wu, Moufeng Wan and Yun Lin

Remote Sens. 2021, 13(16), 3245; https://doi.org/10.3390/rs13163245 - 16 Aug 2021

Cited by 9 | Viewed by 4490

Abstract

Real-time precise point positioning (RT-PPP) has become a powerful technique for the determination of the zenith tropospheric delay (ZTD) over a GPS (global positioning system) or GNSS (global navigation satellite systems) station of interest, and the follow-on high-precision retrieval of precipitable water vapor (PWV). The a priori zenith hydrostatic delay (ZHD) and the mapping function used in the PPP approach are the two factors that could affect the accuracy of the PPP-based ZTD significantly. If the in situ atmospheric pressure is available, the Saastamoinen model can be used to determine ZHD values, and the model-predicted ZHD results are of high accuracy. However, not all GPS/GNSS are equipped with an in situ meteorological sensor. In this research, the daily forecasting ZHD and mapping function values from VMF1 forecasting (VMF1_FC) and VMF3 forecasting (VMF3_FC) products were used for the determination of the GPS-derived PWV. The a priori ZHDs derived from VMF1_FC and VMF3_FC were first evaluated by comparing against the reference ZHDs from globally distributed radiosonde stations. GPS observations from 41 IGS stations that have co-located radiosonde stations during the period of the first half of 2020 were used to test the quality of GPS-ZTD and GPS-PWV. Three sets of ZTDs estimated from RT-PPP solutions using the a priori ZHD and mapping function from the following three VMF products were evaluated: (1) VMF1_FC; (2) VMF3_FC (resolution 5° × 5°); (3) VMF3_FC (resolution 1° × 1°). The results showed that, when the ZHDs from 443 globally distributed radiosonde stations from 1 July 2018 to 30 June 2021 were used as the reference, the mean RMSEs of the ZHDs from the three VMF products were 5.9, 5.4, and 4.3 mm, respectively. The ZTDs estimated from RT-PPP at 41 selected IGS stations were compared with those from IGS, and the results showed that the mean RMSEs of the ZTDs of the 41 stations from the three PPP solutions were 8.6, 9.0, and 8.6 mm, respectively, and the mean RMSEs of the PWV converted from their corresponding ZWDs were 1.9, 2.4, and 1.7 mm, respectively, in comparison with the reference PWV from co-located radiosonde stations. The results suggest that the a priori ZHD and mapping function from VMF1_FC and VMF3_FC can be used for the precise determination of real-time GPS/GNSS-PWV in most regions, especially the VMF3_FC (resolution 1° × 1°) product. Full article

(This article belongs to the Special Issue Satellite Observation for Atmospheric Modeling)

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21 pages, 8297 KiB

Open AccessFeature PaperArticle

Use of GNSS Tropospheric Delay Measurements for the Parameterization and Validation of WRF High-Resolution Re-Analysis over the Western Gulf of Corinth, Greece: The PaTrop Experiment

by Nikolaos Roukounakis, Dimitris Katsanos, Pierre Briole, Panagiotis Elias, Ioannis Kioutsioukis, Athanassios A. Argiriou and Adrianos Retalis

Remote Sens. 2021, 13(10), 1898; https://doi.org/10.3390/rs13101898 - 13 May 2021

Cited by 5 | Viewed by 2848

Abstract

In the last thirty years, Synthetic Aperture Radar interferometry (InSAR) and the Global Navigation Satellite System (GNSS) have become fundamental space geodetic techniques for mapping surface deformations due to tectonic movements. One major limiting factor to those techniques is the effect of the troposphere, as surface velocities are of the order of a few mm yr⁻¹, and high accuracy (to mm level) is required. The troposphere introduces a path delay in the microwave signal, which, in the case of GNSS Precise Point Positioning (PPP), can nowadays be partly removed with the use of specialized mapping functions. Moreover, tropospheric stratification and short wavelength spatial turbulences produce an additive noise to the low amplitude ground deformations calculated by the (multitemporal) InSAR methodology. InSAR atmospheric phase delay corrections are much more challenging, as opposed to GNSS PPP, due to the single pass geometry and the gridded nature of the acquired data. Thus, the precise knowledge of the tropospheric parameters along the propagation medium is extremely useful for the estimation and correction of the atmospheric phase delay. In this context, the PaTrop experiment aims to maximize the potential of using a high-resolution Limited-Area Model for the calculation and removal of the tropospheric noise from InSAR data, by following a synergistic approach and integrating all the latest advances in the fields of remote sensing meteorology (GNSS and InSAR) and Numerical Weather Forecasting (WRF). In the first phase of the experiment, presented in the current paper, we investigate the extent to which a high-resolution 1 km WRF weather re-analysis can produce detailed tropospheric delay maps of the required accuracy, by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is initially operated with varying parameterization, with GNSS measurements providing a benchmark of real atmospheric conditions. Subsequently, the final WRF daily re-analysis run covers an extended period of one year, based on the optimum model parameterization scheme demonstrated by the parametric analysis. The two datasets (predicted and observed) are compared and statistically evaluated, in order to investigate the extent to which meteorological parameters that affect ZTD can be simulated accurately by the model under different weather conditions. Results demonstrate a strong correlation between predicted and observed ZTDs at the 19 GNSS stations throughout the year (R ranges from 0.91 to 0.93), with an average mean bias (MB) of –19.2 mm, indicating that the model tends to slightly underestimate the tropospheric ZTD as compared to the GNSS derived values. With respect to the seasonal component, model performance is better during the autumn period (October–December), followed by spring (April–June). Setting the acceptable bias range at ±23 mm (equal to the amplitude of one Sentinel-1 C-band phase cycle when projected to the zenithal distance), it is demonstrated that the model produces satisfactory results, with a percentage of ZTD values within the bias margin ranging from 57% in summer to 63% in autumn. Full article

(This article belongs to the Special Issue Satellite Observation for Atmospheric Modeling)

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18 pages, 5462 KiB

Open AccessArticle

Impact of Lightning Data Assimilation on the Short-Term Precipitation Forecast over the Central Mediterranean Sea

by Rosa Claudia Torcasio, Stefano Federico, Albert Comellas Prat, Giulia Panegrossi, Leo Pio D'Adderio and Stefano Dietrich

Remote Sens. 2021, 13(4), 682; https://doi.org/10.3390/rs13040682 - 13 Feb 2021

Cited by 21 | Viewed by 4098

Abstract

Lightning data assimilation (LDA) is a powerful tool to improve the weather forecast of convective events and has been widely applied with this purpose in the past two decades. Most of these applications refer to events hitting coastal and land areas, where people live. However, a weather forecast over the sea has many important practical applications, and this paper focuses on the impact of LDA on the precipitation forecast over the central Mediterranean Sea around Italy. The 3 h rapid update cycle (RUC) configuration of the weather research and forecasting (WRF) model) has been used to simulate the whole month of November 2019. Two sets of forecasts have been considered: CTRL, without lightning data assimilation, and LIGHT, which assimilates data from the LIghtning detection NETwork (LINET). The 3 h precipitation forecast has been compared with observations of the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) (IMERG) dataset and with rain gauge observations recorded in six small Italian islands. The comparison of CTRL and LIGHT precipitation forecasts with the IMERG dataset shows a positive impact of LDA. The correlation between predicted and observed precipitation improves over wide areas of the Ionian and Adriatic Seas when LDA is applied. Specifically, the correlation coefficient for the whole domain increases from 0.59 to 0.67, and the anomaly correlation (AC) improves by 5% over land and by 8% over the sea when lightning is assimilated. The impact of LDA on the 3 h precipitation forecast over six small islands is also positive. LDA improves the forecast by both decreasing the false alarms and increasing the hits of the precipitation forecast, although with variability among the islands. The case study of 12 November 2019 (time interval 00–03 UTC) has been used to show how important the impact of LDA can be in practice. In particular, the shifting of the main precipitation pattern from land to the sea caused by LDA gives a much better representation of the precipitation field observed by the IMERG precipitation product. Full article

(This article belongs to the Special Issue Satellite Observation for Atmospheric Modeling)

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28 pages, 3795 KiB

Open AccessArticle

The Impact of Assimilating Satellite Radiance Observations in the Copernicus European Regional Reanalysis (CERRA)

by Zheng Qi Wang and Roger Randriamampianina

Remote Sens. 2021, 13(3), 426; https://doi.org/10.3390/rs13030426 - 26 Jan 2021

Cited by 9 | Viewed by 3213

Abstract

The assimilation of microwave and infrared (IR) radiance satellite observations within numerical weather prediction (NWP) models have been an important component in the effort of improving the accuracy of analysis and forecast. Such capabilities were implemented during the development of the high-resolution Copernicus European Regional Reanalysis (CERRA), funded by the Copernicus Climate Change Services (C3S). The CERRA system couples the deterministic system with the ensemble data assimilation to provide periodic updates of the background error covariance matrix. Several key factors for the assimilation of radiances were investigated, including appropriate use of variational bias correction (VARBC), surface-sensitive AMSU-A observations and observation error correlation. Twenty-one-day impact studies during the summer and winter seasons were conducted. Generally, the assimilation of radiances has a small impact on the analysis, while greater impacts are observed on short-range (12 and 24-h) forecasts with an error reduction of 1–2% for the mid and high troposphere. Although, the current configuration provided less accurate forecasts from 09 and 18 UTC analysis times. With the increased thinning distances and the rejection of IASI observation over land, the errors in the analyses and 3 h forecasts on geopotential height were reduced up to 2%. Full article

(This article belongs to the Special Issue Satellite Observation for Atmospheric Modeling)

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16 pages, 4985 KiB

Open AccessArticle

On the Potential of Improving WRF Model Forecasts by Assimilation of High-Resolution GPS-Derived Water-Vapor Maps Augmented with METEOSAT-11 Data

by Anton Leontiev, Dorita Rostkier-Edelstein and Yuval Reuveni

Remote Sens. 2021, 13(1), 96; https://doi.org/10.3390/rs13010096 - 30 Dec 2020

Cited by 10 | Viewed by 3140

Abstract

Improving the accuracy of numerical weather predictions remains a challenging task. The absence of sufficiently detailed temporal and spatial real-time in-situ measurements poses a critical gap regarding the proper representation of atmospheric moisture fields, such as water vapor distribution, which are highly imperative for improving weather predictions accuracy. The estimated amount of the total vertically integrated water vapor (IWV), which can be derived from the attenuation of global positioning systems (GPS) signals, can support various atmospheric models at global, regional, and local scales. Currently, several existing atmospheric numerical models can estimate the IWV amount. However, they do not provide accurate results compared with in-situ measurements such as radiosondes. Here, we present a new strategy for assimilating 2D IWV regional maps estimations, derived from combined GPS and METEOSAT satellite imagery data, to improve Weather Research and Forecast (WRF) model predictions accuracy in Israel and surrounding areas. As opposed to previous studies, which used point measurements of IWV in the assimilation procedure, in the current study, we assimilate quasi-continuous 2D GPS IWV maps, combined with METEOSAT-11 data. Using the suggested methodology, our results indicate an improvement of more than 30% in the root mean square error (RMSE) of WRF forecasts after assimilation relative standalone WRF, when both are compared to the radiosonde measured data near the Mediterranean coast. Moreover, significant improvements along the Jordan Rift Valley and Dead Sea Valley areas are obtained when compared to 2D IWV regional maps estimations. Improvements in these areas suggest the impact of the assimilated high resolution IWV maps, with initialization times which coincide with the Mediterranean Sea Breeze propagation from the coastline to highland stations, as the distance to the Mediterranean Sea shore, along with other features, dictates its arrival times. Full article

(This article belongs to the Special Issue Satellite Observation for Atmospheric Modeling)

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14 pages, 3318 KiB

Open AccessArticle

Improved Zenith Tropospheric Delay Modeling Using the Piecewise Model of Atmospheric Refractivity

by Liu Yang, Jingxiang Gao, Dantong Zhu, Nanshan Zheng and Zengke Li

Remote Sens. 2020, 12(23), 3876; https://doi.org/10.3390/rs12233876 - 26 Nov 2020

Cited by 13 | Viewed by 2675

Abstract

As one of the atmosphere propagation delays, the tropospheric delay is a significant error source that should be properly handled in high-precision global navigation satellite system (GNSS) applications. We propose an improved zenith tropospheric delay (ZTD) modeling method whereby the piecewise model of the atmospheric refractivity is introduced. Compared with using the exponential model to fit ZTD in vertical direction, the ZTD piecewise model has a better performance. Based on ERA5 2.5° × 2.5° reanalysis data produced by the European Centre for Medium-Range Weather Forecasting (ECMWF) from 2013 to 2017, we establish the regional gridded ZTD model (RGZTD) using a trigonometric function for China and the surrounding areas, which ranges from 70° E to 135° E in longitude and from 15° N to 55° N in latitude. To verify the performance of RGZTD model, the ERA5 ZTD data in 2017–2018, the radiosonde ZTD data from 86 radiosonde stations over China in 2017–2018, and the tropospheric delay products on 251 GNSS stations from Crustal Movement Observation Network of China (CMONOC) in 2016–2017 are used as external compliance check data. The results show that the overall accuracy of RGZTD model is better than that of exponential model, UNB3m model, and GPT3 model. Moreover, the accuracy can be improved by about 13.4%, 7.1%, and 6.2% when ERA5 reanalysis data, radiosonde data, and CMONOC data are used as reference values, respectively. High-accuracy ZTD data can be provided because the RGZTD model takes into account the vertical variation of ZTD through the new piecewise model. Full article

(This article belongs to the Special Issue Satellite Observation for Atmospheric Modeling)

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16 pages, 6675 KiB

Open AccessCase Report

A Satellite View of an Intense Snowfall in Madrid (Spain): The Storm ‘Filomena’ in January 2021

by Francisco J. Tapiador, Anahí Villalba-Pradas, Andrés Navarro, Raúl Martín, Andrés Merino, Eduardo García-Ortega, José Luis Sánchez, Kwonil Kim and Gyuwon Lee

Remote Sens. 2021, 13(14), 2702; https://doi.org/10.3390/rs13142702 - 9 Jul 2021

Cited by 9 | Viewed by 3257

Abstract

Evaluating satellite ability in capturing sudden natural disasters such as heavy snowstorms is a topic of societal interest. This paper presents a rapid qualitative analysis of an intense snowfall in Madrid using data from the Global Precipitation Measurement (GPM) mission, specifically the GPM IMERG (Integrated Multi-satellitE Retrievals for GPM) Late Precipitation L3 Half Hourly 0.1° × 0.1° V06 estimates of precipitation (IMERG-Late), and Sentinel-2 imagery. The main research question addressed is the consistency of ground observations, model outputs and satellite data, a topic of major interest for an appropriate and timely societal response to severe weather episodes. Indeed, the choice of the ‘Late’ product over the IMERG ‘Final’ or other GPM datasets was motivated by the availability of data for near real-time response to the storm. Additionally, the 30-min temporal resolution of the product would in principle allow for a detailed analysis of the dynamic processes involved in the snowstorm. Using several complementary data sources, it is shown that optical remote sensing sensors (Sentinel) add value to existing ground data and that is invaluable for rapid response to severe meteorological events such as Filomena. Regarding the GPM precipitation radar, the sampling of the GPM-core satellite was insufficient to provide the IMERG algorithm with enough quality data to correctly represent the actual sequence of precipitation. Without corrections, the total precipitation differs from observations by a factor of two. The difficulties of retrieving precipitation with radiometers over snow-covered surfaces was a major factor for the mismatch. Thus, the calibrated precipitation product did not fully capture the historic storm, and neither did the IR-based element of the IMERG-Late product, which is a neural network merging of microwave and infrared data. It follows that increased temporal resolution of spaceborne microwave sensors and improved retrieval of precipitation from radiometers are critical in order to provide a complete account of these sorts of extreme, significant, short-duration cases. Otherwise, the high-quality, radar and radiometer data feeding the high temporal resolution algorithms simply slip through the grasp of the ascending and descending orbits, leaving little quality data to be interpolated into successive overpasses. Full article

(This article belongs to the Special Issue Satellite Observation for Atmospheric Modeling)

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15 pages, 5998 KiB

Open AccessTechnical Note

Comparison of Vertically Integrated Fluxes of Atmospheric Water Vapor According to Satellite Radiothermovision, Radiosondes, and Reanalysis

by Dmitry Ermakov, Alexey Kuzmin, Evgeny Pashinov, Victor Sterlyadkin, Andrey Chernushich and Eugene Sharkov

Remote Sens. 2021, 13(9), 1639; https://doi.org/10.3390/rs13091639 - 22 Apr 2021

Cited by 5 | Viewed by 2413

Abstract

The atmospheric advection of water vapor is one of the most important components of the planetary hydrological cycle. Radiosondes are a means for regular observations of water vapor fluxes. However, their data are sparse in space and time. A more complete picture is provided by reanalysis assimilating these data. However, a statistically representative check of the reanalysis estimates of the water vapor fluxes far from regularly operating weather stations is difficult. The previously proposed and developed method of satellite radiothermovision makes it possible to reconstruct the vertically integrated advective water vapor fluxes from satellite microwave radiometry. In this work, for the first time, the results of direct comparisons of long (5 year) time series of zonal vertically integrated daily water vapor fluxes based on the data of radiosondes, reanalysis, and satellite radiothermovision are performed and presented. It is shown that all the data series are statistically reliably correlated (at a confidence level of 0.995). The regression factor between the fluxes from reanalysis and satellite radiothermovision was close to 1, but with a noticeable bias (the latter were about 60 kg/(m·s) less on average). Grounds are given for the hypothesis that calculations based on satellite radiothermovision mainly characterize water vapor fluxes in the lower troposphere (up to heights of about 4 km). Its verification, as well as the analysis of the noted cases of violation of the correlation between the fluxes from satellite radiothermovision and reanalysis, requires further research. Full article

(This article belongs to the Special Issue Satellite Observation for Atmospheric Modeling)

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