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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 2020) | Viewed by 41866

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Dr. Marina Astitha

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Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: atmospheric physics, dynamics, and chemistry; prediction of extreme weather events; integration of multimedia modeling systems using machine learning; real-time weather and air quality forecasting; uncertainties in atmospheric and air quality modeling systems
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Dear Colleagues,

Weather forecasting employs numerical weather prediction, which has evolved through increased computational power, the ingestion of observations from various platforms (in-situ and remote sensing), multi-agency and international collaborations, and advancements in the representation of physics and dynamics in the model structure. We increasingly rely on weather forecasts to protect life, infrastructure, and the environment—especially in the event of imminent extreme atmospheric conditions. Moreover, our capability to observe Earth from space over the last 60 years has catalyzed our understanding of the dynamic processes that govern air, ocean, land, and soil at various spatial and temporal scales. Space-based observations are used not only to understand the planet, but also to make assessments and predictions of major environmental problems such as eutrophication, extreme storms, precipitation, wildfires, ice melt, and sea level rise, among others. Satellite observations combined with numerical weather prediction models and data assimilation techniques have become essential components in the fully coupled Earth system framework (NAS, 2018), and will continue to be in the future. In the event of extreme weather phenomena such as hurricanes, we rely on satellites to track the location and intensity of the storm and inform weather prediction systems in real-time to provide more accurate forecasts for the next 3–7 day outlook.

This Special Issue “Weather Forecasting and Modeling Using Satellite Data” aims to bring together current state-of-the-art research about the use of geostationary and/or polar orbiting satellite data in weather prediction from short-term to sub-seasonal and seasonal scales. Weather prediction can refer to deterministic or probabilistic frameworks with single or multi-model ensembles that utilize satellite data and/or develop new techniques to integrate the two and improve weather forecasts. Research related to the above topics will be considered for publication in Remote Sensing under the Special Issue.

Prof. Marina Astitha
Guest Editor

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Keywords

numerical weather prediction
satellite data
extreme weather events
data assimilation
verification
seasonal forecast
short-term forecast

Published Papers (9 papers)

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Research

25 pages, 32044 KiB

Open AccessArticle

On a Flood-Producing Coastal Mesoscale Convective Storm Associated with the Kor’easterlies: Multi-Data Analyses Using Remotely-Sensed and In-Situ Observations and Storm-Scale Model Simulations

by Seon Ki Park and Sojung Park

Remote Sens. 2020, 12(9), 1532; https://doi.org/10.3390/rs12091532 - 11 May 2020

Cited by 7 | Viewed by 4226

Abstract

A flood-producing heavy rainfall event occurred at the mountainous coastal region in the northeast of South Korea on 5–6 August 2018, subsequent to extreme heat waves, through a quasi-stationary mesoscale convective system (MCS). We analyzed the storm environment via a multi-data approach using high-resolution (1-km) simulations from the Weather Research and Forecasting (WRF) and in situ/satellite/radar observations. The brightness temperature, from the Advanced Himawari Imager water vapor band, and the composite radar reflectivity were used to identify characteristics of the MCS and associated precipitations. The following factors affected this back-building MCS: low-level convergence by the Korea easterlies (Kor’easterlies), carrying moist air into the coast; strong vertical wind shear, making the updraft tilted and sustained; coastal fronts and back-building convection bands, formed through interactions among the Kor’easterlies, cold pool outflows, and orography; mid-level advection of cold air and positive relative vorticity, enhancing vertical convection and potential instability; and vigorous updraft releasing potential instability. The pre-storm synoptic environment provided favorable conditions for storm development such as high moisture and temperature over the coastal area and adjacent sea, and enhancement of the Kor’easterlies by expansion of a surface high pressure system. Upper-level north-northwesterly winds prompted the MCS to propagate south-southeastward along the coastline. Full article

(This article belongs to the Special Issue Weather Forecasting and Modeling Using Satellite Data)

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23 pages, 12284 KiB

Open AccessArticle

The Role of Sea Surface Temperature Forcing in the Life-Cycle of Mediterranean Cyclones

by Christos Stathopoulos, Platon Patlakas, Christos Tsalis and George Kallos

Remote Sens. 2020, 12(5), 825; https://doi.org/10.3390/rs12050825 - 03 Mar 2020

Cited by 14 | Viewed by 3431

Abstract

Air–sea interface processes are highly associated with the evolution and intensity of marine-developed storms. Specifically, in the Mediterranean Sea, the air–ocean temperature deviations have a profound role during the several stages of Mediterranean cyclonic events. Subsequently, this enhances the need for better knowledge and representation of the sea surface temperature (SST). In this work, an analysis of the impact and uncertainty of the SST from different well-known datasets on the life-cycle of Mediterranean cyclones is attempted. Daily SST from the Real Time Global SST (RTG_SST) and hourly SST fields from the Operational SST and Sea Ice Ocean Analysis (OSTIA) and the NEMO ocean circulation model are implemented in the RAMS/ICLAMS-WAM coupled modeling system. For the needs of the study, the Mediterranean cyclones Trixi, Numa, and Zorbas were selected. Numerical experiments covered all stages of their life-cycles (five to seven days). Model results have been analyzed in terms of storm tracks and intensities, cyclonic structural characteristics, and derived heat fluxes. Remote sensing data from the Integrated Multi-satellitE Retrievals (IMERG) for Global Precipitation Measurements (GPM), Blended Sea Winds, and JASON altimetry missions were employed for a qualitative and quantitative comparison of modeled results in precipitation, maximum surface wind speed, and wave height. Spatiotemporal deviations in the SST forcing rather than significant differences in the maximum/minimum SST values, seem to mainly contribute to the differences between the model results. Considerable deviations emerged in the resulting heat fluxes, while the most important differences were found in precipitation exhibiting spatial and intensity variations reaching 100 mm. The employment of widely used products is shown to result in different outcomes and this point should be taken into consideration in forecasting and early warning systems. Full article

(This article belongs to the Special Issue Weather Forecasting and Modeling Using Satellite Data)

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20 pages, 6681 KiB

Open AccessArticle

Data Assimilation of High-Resolution Satellite Rainfall Product Improves Rainfall Simulation Associated with Landfalling Tropical Cyclones in the Yangtze River Delta

by Jie Wang, Youpeng Xu, Long Yang, Qiang Wang, Jia Yuan and Yuefeng Wang

Remote Sens. 2020, 12(2), 276; https://doi.org/10.3390/rs12020276 - 14 Jan 2020

Cited by 15 | Viewed by 3597

Abstract

Floods caused by heavy rainfall events associated with landfalling tropical cyclones (TCs) represent a major risk for the Yangtze River Delta (YRD) region of China. Accurate extreme precipitation forecasting, at long lead times, is crucial for the improvement of flood prevention and warning. However, accurate prediction of timing, location, and intensity of the heavy rainfall events is a major challenge for the Numerical Weather Prediction (NWP). In this study, high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) are evaluated at the hourly timescale, and the optimal Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product is selected and applied to directly assimilate into the Weather Research and Forecasting (WRF) model via the four-dimensional variational (4D-Var) method. The TC Jondari and Rumbia events of August 2018 are evaluated to analyze the performance of the WRF model with the 4D-Var method assimilated IMERG precipitation product (DA-IMERG) and the conventional observation (DA-CONV) for real-time heavy rainfall forecasting. The results indicate that (1) IMERG precipitation products were larger and wetter than the observed precipitation values over YRD. By comparison, the performance of “late” run precipitation product (IMERG-L) was the closest to the observation data with lower deviation and higher detection capability; (2) DA-IMERG experiment substantially affected the magnitude of the WRF model primary variables, which changed the precipitation pattern of the TC heavy rain. (3) DA-IMERG experiment further improved the forecast of heavy rainbands and relatively reduced erroneous detection rate than CTL and DA-CONV experiments at the grid scale. Meanwhile, the DA-IMERG experiment has a better fractions skill score (FSS) value (especially in the threshold of 10 mm/h) than DA-CONV for TC Jondari and Rumbia at the spatial scale, while it shows a lower performance than CTL and DA-CONV experiments when the threshold is lower than the 5 mm/h for the TC Rumbia. Full article

(This article belongs to the Special Issue Weather Forecasting and Modeling Using Satellite Data)

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14 pages, 829 KiB

Open AccessArticle

GPS-PWV Based Improved Long-Term Rainfall Prediction Algorithm for Tropical Regions

by Shilpa Manandhar, Yee Hui Lee and Yu Song Meng

Remote Sens. 2019, 11(22), 2643; https://doi.org/10.3390/rs11222643 - 12 Nov 2019

Cited by 24 | Viewed by 2960

Abstract

Global positioning system (GPS) satellite delay is extensively used in deriving the precipitable water vapor (PWV) with high spatio–temporal resolution. One of the recent applications of GPS derived PWV values are to predict rainfall events. In the literature, there are rainfall prediction algorithms based on GPS-PWV values. Most of the algorithms are developed using data from temperate and sub-tropical regions. Mostly these algorithms use maximum PWV rate, maximum PWV variation and monthly PWV values as a criterion to predict the rain events. This paper examines these algorithms using data from the tropical stations and proposes the use of maximum PWV value for better prediction. When maximum PWV value and maximum rate of increment criteria are implemented on the data from the tropical stations, the false alarm (

F A

) rate is reduced by almost 17% as compared to the results from the literature. There is a significant reduction in

F A

rates while maintaining the true detection (

T D

) rates as high as that of the literature. A study done on the varying historical length of data and lead time values shows that almost 80% of the rainfall can be predicted with a false alarm of

26.4

% for a historical data length of 2 hours and a lead time of 45 min to 1 hour. Full article

(This article belongs to the Special Issue Weather Forecasting and Modeling Using Satellite Data)

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38 pages, 25893 KiB

Open AccessArticle

A Synergistic Use of a High-Resolution Numerical Weather Prediction Model and High-Resolution Earth Observation Products to Improve Precipitation Forecast

by Martina Lagasio, Antonio Parodi, Luca Pulvirenti, Agostino N. Meroni, Giorgio Boni, Nazzareno Pierdicca, Frank S. Marzano, Lorenzo Luini, Giovanna Venuti, Eugenio Realini, Andrea Gatti, Giulio Tagliaferro, Stefano Barindelli, Andrea Monti Guarnieri, Klodiana Goga, Olivier Terzo, Alessio Rucci, Emanuele Passera, Dieter Kranzlmueller and Bjorn Rommen

Remote Sens. 2019, 11(20), 2387; https://doi.org/10.3390/rs11202387 - 15 Oct 2019

Cited by 39 | Viewed by 5425

Abstract

The Mediterranean region is frequently struck by severe rainfall events causing numerous casualties and several million euros of damages every year. Thus, improving the forecast accuracy is a fundamental goal to limit social and economic damages. Numerical Weather Prediction (NWP) models are currently able to produce forecasts at the km scale grid spacing but unreliable surface information and a poor knowledge of the initial state of the atmosphere may produce inaccurate simulations of weather phenomena. The STEAM (SaTellite Earth observation for Atmospheric Modelling) project aims to investigate whether Sentinel satellites constellation weather observation data, in combination with Global Navigation Satellite System (GNSS) observations, can be used to better understand and predict with a higher spatio-temporal resolution the atmospheric phenomena resulting in severe weather events. Two heavy rainfall events that occurred in Italy in the autumn of 2017 are studied—a localized and short-lived event and a long-lived one. By assimilating a wide range of Sentinel and GNSS observations in a state-of-the-art NWP model, it is found that the forecasts benefit the most when the model is provided with information on the wind field and/or the water vapor content. Full article

(This article belongs to the Special Issue Weather Forecasting and Modeling Using Satellite Data)

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19 pages, 8791 KiB

Open AccessArticle

Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation

by Shuxian Liu, Zhigang Chu, Yan Yin and Ruixia Liu

Remote Sens. 2019, 11(20), 2338; https://doi.org/10.3390/rs11202338 - 09 Oct 2019

Cited by 2 | Viewed by 2150

Abstract

Accurate precipitation detection is one of the most important factors in satellite data assimilation, due to the large uncertainties associated with precipitation properties in radiative transfer models and numerical weather prediction (NWP) models. In this paper, a method to achieve remote sensing of precipitation and classify its intensity over land using a co-located ground-based radar network is described. This method is intended to characterize the O−B biases for the microwave humidity sounder -2 (MWHS-2) under four categories of precipitation: precipitation-free (0–5 dBZ), light precipitation (5–20 dBZ), moderate precipitation (20–35 dBZ), and intense precipitation (>35 dBZ). Additionally, O represents the observed brightness temperature (TB) of the satellite and B is the simulated TB from the model background field using the radiative transfer model. Thresholds for the brightness temperature differences between channels, as well as the order relation between the differences, exhibited a good estimation of precipitation. It is demonstrated that differences between observations and simulations were predominantly due to the cases in which radar reflectivity was above 15 dBZ. For most channels, the biases and standard deviations of O−B increased with precipitation intensity. Specifically, it is noted that for channel 11 (183.31 ± 1 GHz), the standard deviations of O−B under moderate and intense precipitation were even smaller than those under light precipitation and precipitation-free conditions. Likewise, abnormal results can also be seen for channel 4 (118.75 ± 0.3 GHz). Full article

(This article belongs to the Special Issue Weather Forecasting and Modeling Using Satellite Data)

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23 pages, 5637 KiB

Open AccessArticle

Observing System Experiments with an Arctic Mesoscale Numerical Weather Prediction Model

by Roger Randriamampianina, Harald Schyberg and Máté Mile

Remote Sens. 2019, 11(8), 981; https://doi.org/10.3390/rs11080981 - 24 Apr 2019

Cited by 39 | Viewed by 4023

Abstract

In the Arctic, weather forecasting is one element of risk mitigation, helping operators to have knowledge on weather-related risk in advance through forecasting capabilities at time ranges from a few hours to days ahead. The operational numerical weather prediction is an initial value problem where the forecast quality depends both on the quality of the forecast model itself and on the quality of the specified initial state. The initial states are regularly updated using environmental observations through data assimilation. This paper assesses the impact of observations, which are accessible through the global telecommunication and the EUMETCast dissemination systems on analyses and forecasts of an Arctic limited area AROME (Application of Research to Operations at Mesoscale) model (AROME-Arctic). An assessment through the computation of degrees of freedom for signals on the analysis, the utilization of an energy norm-based approach applied to the forecasts, verifications against observations, and a case study showed similar impacts of the studied observations on the AROME-Arctic analysis and forecast systems. The AROME-Arctic assimilation system showed a relatively high sensitivity to the humidity or humidity-sensitive observations. The more radiance data were assimilated, the lower was the estimated relative sensitivity of the assimilation system to different conventional observations. Data assimilation, at least for surface parameters, is needed to produce accurate forecasts from a few hours up to days ahead over the studied Arctic region. Upper-air conventional observations are not enough to improve the forecasting capability over the AROME-Arctic domain compared to those already produced by the ECMWF (European Centre for Medium-range Weather Forecast). Each added radiance data showed a relatively positive impact on the analyses and forecasts of the AROME-Arctic. The humidity-sensitive microwave (AMSU-B/MHS) radiances, assimilated together with the conventional observations and the Infrared Atmospheric Sounding Interferometer (IASI)-assimilated on top of conventional and microwave radiances produced enough accurate one-day-ahead forecasts of polar low. Full article

(This article belongs to the Special Issue Weather Forecasting and Modeling Using Satellite Data)

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18 pages, 6356 KiB

Open AccessArticle

Assessment of Satellite-Based Precipitation Measurement Products over the Hot Desert Climate of Egypt

by Mohamed Salem Nashwan, Shamsuddin Shahid and Xiaojun Wang

Remote Sens. 2019, 11(5), 555; https://doi.org/10.3390/rs11050555 - 07 Mar 2019

Cited by 74 | Viewed by 7159

Abstract

The performance of three satellite-based high-resolution gridded rainfall datasets, namely the gauge corrected Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) in the hot desert climate of Egypt were assessed. Seven statistical indices including four categorical indices were used to assess the capability of the products in estimating the daily rainfall amounts and detecting the occurrences of rainfall under different intensity classes from March 2014 to May 2018. Although the products were gauge-corrected, none of them showed a consistent performance, and thus could not be titled as the best or worst performing product over Egypt. The CHIRPS was found to be the best product in estimating rainfall amounts when all rainfall events were considered and IMERG was found as the worst. However, IMERG was better at detecting the occurrence of rainfall than CHIRPS. For heavy rainfall events, IMERG was better at the majority of the stations in terms of the Kling–Gupta efficiency index (−0.34) and skill-score (0.33). The IMERG was able to show the spatial variability of rainfall during the recent big flash flood event that hit Northern Egypt. The study indicates that accurate estimation of rainfall in the hot desert climate using satellite sensors remains a challenge. Full article

(This article belongs to the Special Issue Weather Forecasting and Modeling Using Satellite Data)

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20 pages, 4222 KiB

Open AccessArticle

Local Severe Storm Tracking and Warning in Pre-Convection Stage from the New Generation Geostationary Weather Satellite Measurements

by Zijing Liu, Min Min, Jun Li, Fenglin Sun, Di Di, Yufei Ai, Zhenglong Li, Danyu Qin, Guicai Li, Yinjing Lin and Xiaolin Zhang

Remote Sens. 2019, 11(4), 383; https://doi.org/10.3390/rs11040383 - 13 Feb 2019

Cited by 23 | Viewed by 4496

Abstract

Accurate and prior identification of local severe storm systems in pre-convection environments using geostationary satellite imagery measurements is a challenging task. Methodologies for “convective initiation” identification have already been developed and explored for operational nowcasting applications; however, warning of such convective systems using the new generation of geostationary satellite imagery measurements in pre-convection environments is still not well studied. In this investigation, the Random Forest (RF) machine learning algorithm is used to develop a predictive statistical model for tracking and identifying three different types of convective storm systems (weak, medium, and severe) over East Asia by combining spatially-temporally collocated Himawari-8 (H08) measurements and Numerical Weather Prediction (NWP) forecast data. The Global Precipitation Measurement (GPM) gridded product is used as a benchmark to train the predictive models based on a sample-balance technique which can adjust or balance the samples of three different convection types to avoid over-fitting any type of dataset. Variables such as brightness temperatures (BTs) from H08 water vapor absorption bands (6.2 μm, 6.9 μm and 7.3 μm) and Total Precipitable Water (TPW) from NWP show relatively high ranks in the predictive model training. These sensitive variables are closely associated with convectively dominated precipitation areas, indicating the importance of predictors from both H08 and NWP data. The final optimal RF model is achieved with an accuracy of 0.79 for classification of all convective storm systems, while the Probability of Detection (POD) of this model for severe and medium convections can reach 0.66 and 0.70, respectively. Two typical sudden convective storm cases in the warm season of 2018 tracked by this algorithm are described, and results indicate that the H08 and NWP based statistical model using the RF algorithm is capable of capturing local burst convective storm systems about 1–2 h earlier than the outbreak of heavy rainfall. Full article

(This article belongs to the Special Issue Weather Forecasting and Modeling Using Satellite Data)

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