Journal articles on the topic 'Smog Forecasting'
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Hess, G. D., K. J. Tory, M. E. Cope, S. Lee, K. Puri, P. C. Manins, and M. Young. "The Australian Air Quality Forecasting System. Part II: Case Study of a Sydney 7-Day Photochemical Smog Event." Journal of Applied Meteorology 43, no. 5 (May 1, 2004): 663–79. http://dx.doi.org/10.1175/2094.1.
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Abstract The performance of the Australian Air Quality Forecasting System (AAQFS) is examined by means of a case study of a 7-day photochemical smog event in the Sydney region. This was the worst smog event for the 2000/ 01 oxidant season, and, because of its prolonged nature, it provided the opportunity to demonstrate the ability of AAQFS to forecast situations involving recirculation of precursors and remnant ozone, fumigation, and complex meteorological dynamics. The forecasting system was able to successfully predict high values of ozone, although at times the peak concentrations for the inland stations were underestimated. The dynamics for the Sydney region require a sensitive balance between the synoptic and mesoscale flows. Often high concentrations of ozone were advected inland by the sea breeze. On two occasions the system forecast a synoptic flow that was too strong, which blocked the inland advancement of the sea breeze. The peak ozone forecasts were underpredicted at the inland stations on those occasions. An examination of possible factors causing forecast errors has indicated that the AAQFS is more sensitive to errors in the meteorological conditions, rather than in the emissions or chemical mechanism in the Sydney region.
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Chen, Jiaoyan, Huajun Chen, Zhaohui Wu, Daning Hu, and Jeff Z. Pan. "Forecasting smog-related health hazard based on social media and physical sensor." Information Systems 64 (March 2017): 281–91. http://dx.doi.org/10.1016/j.is.2016.03.011.
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Sipakov, Rostyslav, Olena Voloshkina, Volodimir Trofimovich, and Julia Bereznitska. "IMPACT OF WEATHER FACTORS ON THE SPEED OF THE REACTION OF FORMALDEHYDE FORMATION ABOVE MOTORWAY OVERPASSES." DSpace at USEFUL.academy, no. 2018 (July 2018): 97–102. http://dx.doi.org/10.32557/issn.2640-9631/2018-3.
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The analysis of the actual air condition in the city of Kyiv in the areas of big overpasses and crossroads shows that the average annual concentration of formaldehyde more than 3 times exceeds the maximum permissible concentration (MPC) of this toxic substance. One of the most powerful sources of formaldehyde formation in the air of the city is motor vehicles. The role of weather factors in formaldehyde formation rate (K) depending on capacity of emissions of internal combustion engines has been analyzed in this article. The equation for determining rate constant has been obtained, which depends on the temperature in the city of Kyiv and on the value of effective energy activation of the mentioned process. The comparison of the calculated and measured values of the rate reaction constant in Observation Point (OP) No. 9 in the area of Leningradska square has been given. The conducted research gives the opportunity to assess and obtain forecasting data on atmospheric pollution and probability of smog situations emergence in Kyiv. The suggested methodology can be used for other cities in Ukraine where motor vehicles are the main indicator of photochemical smog emergence.
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Abdul-Wahab, S. A. "IER Photochemical Smog Evaluation and Forecasting of Short-Term Ozone Pollution Levels with Artificial Neural Networks." Process Safety and Environmental Protection 79, no. 2 (March 2001): 117–28. http://dx.doi.org/10.1205/09575820151095201.
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Businger, Steven, Roy Huff, Andre Pattantyus, Keith Horton, A. Jeff Sutton, Tamar Elias, and Tiziana Cherubini. "Observing and Forecasting Vog Dispersion from Kīlauea Volcano, Hawaii." Bulletin of the American Meteorological Society 96, no. 10 (October 1, 2015): 1667–86. http://dx.doi.org/10.1175/bams-d-14-00150.1.
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Abstract Emissions from Kīlauea volcano, known locally as “vog” for volcanic smog, pose significant environmental and health risks to the Hawaiian community. The Vog Measurement and Prediction (VMAP) project was conceived to help mitigate the negative impacts of Kīlauea’s emissions. To date, the VMAP project has achieved the following milestones: i) created a custom application of the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT, hereafter Vog model) to produce statewide forecasts of the concentration and dispersion of sulfur dioxide (SO2) and sulfate aerosol from Kīlauea volcano; ii) developed an ultraviolet (UV) spectrometer array to provide near-real-time volcanic gas emission rate measurements for use as input into the Vog model; iii) developed and deployed a stationary array of ambient SO2 and meteorological sensors to record the spatial characteristics of Kīlauea’s gas plume in high temporal and spatial resolution for model verification; and iv) developed web-based tools to facilitate the dissemination of observations and model forecasts to provide guidance for safety officials and the public, and to raise awareness of the potential hazards of volcanic emissions to respiratory health, agriculture, and general aviation. Wind fields and thermodynamic data from the Weather Research and Forecasting (WRF) Model provide input to the Vog model, with a statewide grid spacing of 3 km and a 1-km grid covering the island of Hawaii. Validation of the Vog model forecasts is accomplished with reference to data from Hawaii State Department of Health ground-based air quality monitors. VMAP results show that this approach can provide useful guidance for the people of Hawaii.
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Holland, Lacey, Steven Businger, Tamar Elias, and Tiziana Cherubini. "Two Ensemble Approaches for Forecasting Sulfur Dioxide Concentrations from Kīlauea Volcano." Weather and Forecasting 35, no. 5 (October 1, 2020): 1923–37. http://dx.doi.org/10.1175/waf-d-19-0189.1.
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AbstractKīlauea volcano, located on the island of Hawaii, is one of the most active volcanoes in the world. It was in a state of nearly continuous eruption from 1983 to 2018 with copious emissions of sulfur dioxide (SO2) that affected public health, agriculture, and infrastructure over large portions of the island. Since 2010, the University of Hawaiʻi at Mānoa provides publicly available vog forecasts that began in 2010 to aid in the mitigation of volcanic smog (or “vog”) as a hazard. In September 2017, the forecast system began to produce operational ensemble forecasts. The months that preceded Kīlauea’s historic lower east rift zone eruption of 2018 provide an opportunity to evaluate the newly implemented air quality ensemble prediction system and compare it another approach to the generation of ensemble members. One of the two approaches generates perturbations in the wind field while the other perturbs the sulfur dioxide (SO2) emission rate from the volcano. This comparison has implications for the limits of forecast predictability under the particularly dynamic conditions at Kīlauea volcano. We show that for ensemble forecasts of SO2 generated under these conditions, the uncertainty associated with the SO2 emission rate approaches that of the uncertainty in the wind field. However, the inclusion of a fluctuating SO2 emission rate has the potential to improve the prediction of the changes in air quality downwind of the volcano with suitable postprocessing.
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Tory, K. J., M. E. Cope, G. D. Hess, S. Lee, K. Puri, P. C. Manins, and N. Wong. "The Australian Air Quality Forecasting System. Part III: Case Study of a Melbourne 4-Day Photochemical Smog Event." Journal of Applied Meteorology 43, no. 5 (May 1, 2004): 680–95. http://dx.doi.org/10.1175/2092.1.
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Abstract A 4-day photochemical smog event in the Melbourne, Victoria, Australia, region (6–9 March 2001) is examined to assess the performance of the Australian Air Quality Forecasting System (AAQFS). Although peak ozone concentrations measured during this period did not exceed the 1-h national air quality standard of 100 ppb, elevated maximum ozone concentrations in the range of 50–80 ppb were recorded at a number of monitoring stations on all four days. These maximum values were in general very well forecast by the AAQFS. On all but the third day the system predicted the advection of ozone precursors over Port Phillip (the adjacent bay) during the morning, where, later in the day, relatively high ozone concentrations developed. The ozone was advected back inland by bay and sea breezes. On the third day, a southerly component to the background wind direction prevented the precursor drainage over the bay, and the characteristic ozone cycle was disrupted. The success of the system's ability to predict peak ozone at individual monitoring stations was largely dependent on the direction and penetration of the sea and bay breezes, which in turn were dependent on the delicate balance between these winds and the opposing synoptic flow.
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Rasid, N., M. Prashnani, J. Goswami, and P. L. N. Raju. "CROP DAMAGE ASSESSMENT IN FLOOD INUNDATED AREA OF MORIGAON DISTRICT OF ASSAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 489–91. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-489-2019.
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<p><strong>Abstract.</strong> Cropping System Analysis is essential for studying the sustainability of Agriculture. Remote sensing technology provide continuous and synoptic observations of crop area over large extent and substantial contribution in monitoring, evaluating and forecasting of crop and its damage assessment both in cloud and cloud-free environment. Geo-stationary satellites like Synthetic Aperture Radar (SAR) can penetrate through clouds hence it help in assessment of crop even in hazy atmospheric circumstances like fog, smog, light rain, mist etc. The present study reviews cropping pattern and crop rotation of of Morigaon District of Assam. Landsat OLI- 8 multi-spectral data , sentinel 2 multi-spectral and sentinel- 1 SAR data was collected during crop year 2015, 2016 and 2017. The microwave SAR data was used for the classification of crop area for Kharif season due to unavailability of optical cloud free data and also helps in estimation of flood water propagation and its extent and its significant loss to agriculture crop. The result of the pilot study shows that integration of SAR data and GIS environment can be exploited in an efficient way to assess the crop damage area due to flood. Block-wise flood inundation statistics have been derived. This study can be extended to other states/ districts as data collected by satellite can be standardized, the data are reliably objective.</p>
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Foszcz, Dariusz, Tomasz Niedoba, and Jarosław Siewior. "Models of Air Pollution Propagation in the Selected Region of Katowice." Atmosphere 12, no. 6 (May 29, 2021): 695. http://dx.doi.org/10.3390/atmos12060695.
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The paper deals with issues related to analyzing the spread of air pollution and pollutants in large urban agglomerations, specifically, the search for causality between meteorological conditions and the concentrations of particular substances. The pollutants SO2 and PM10 were selected for analysis, which, in addition to NOx, CO, CO2 and PM2.5, contribute to smog, especially during the heating seasons. This analysis is particularly important because Polish environmental standards are more lenient than those in western EU states. Industrial activity, transport and heating systems based on coal-burning are still a big problem in Poland, and each year their gaseous and particulate emissions exceed air-quality limits. This paper presents a statistical analysis of data recorded at the air-quality monitoring station on Kossuth Street in Katowice concerning the heating seasons from 2013–2016. The verification of proposed parabolic models containing concentrations from previous time periods and statistically significant meteorological conditions was conducted for individual heating seasons as well for the whole set of data, which included the influence of wind speed and temperature. The models obtained proved that the selected form of a model is statistically significant, and its use may produce satisfactory forecast results and permit various environmental applications. The specified model might be used both for forecasting (verification and possibly updating coefficients to increase forecast accuracy) and analyzing the factors influencing pollution values. Such statistical analysis may be helpful in assessing the impact of measures adopted to reduce air pollution, particularly in large Polish cities.
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Hu, Jun, Yichen Li, Tianliang Zhao, Jane Liu, Xiao-Ming Hu, Duanyang Liu, Yongcheng Jiang, Jianming Xu, and Luyu Chang. "An important mechanism of regional O<sub>3</sub> transport for summer smog over the Yangtze River Delta in eastern China." Atmospheric Chemistry and Physics 18, no. 22 (November 15, 2018): 16239–51. http://dx.doi.org/10.5194/acp-18-16239-2018.
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Abstract. Severe ozone (O3) pollution episodes plague a few regions in eastern China at certain times of the year, e.g., the Yangtze River Delta (YRD). However, the formation mechanisms, including meteorological factors, contributing to these severe pollution events remain elusive. A severe summer smog stretched over the YRD region from 22 to 25 August 2016. This event displayed hourly surface O3 concentrations that exceeded 300 µg m−3 on 25 August in Nanjing, an urban area in the western YRD. The weather pattern during this period was characterized by near-surface prevailing easterly winds and continuous high air temperatures. The formation mechanism responsible for this O3 pollution episode over the YRD region, particularly the extreme values over the western YRD, was investigated using observation data and by running simulations with the Weather Research and Forecasting model with Chemistry (WRF-Chem). The results showed that the extremely high surface O3 concentration in the western YRD area on 25 August was largely due to regional O3 transport in the nocturnal residual layer (RL) and the diurnal change in the atmospheric boundary layer. On 24 August, high O3 levels, with peak values of 220 µg m−3, occurred in the daytime mixing layer over the eastern YRD region. During nighttime from 24 to 25 August, a shallow stable boundary layer formed near the surface which decoupled the RL above it from the surface. Ozone in the decoupled RL remained quite constant, which resulted in an O3-rich “reservoir” forming in this layer. This reservoir persisted due to the absence of O3 consumption from nitrogen oxide (NO) titration or dry deposition during nighttime. The prevailing easterly winds in the lower troposphere governed the regional transport of this O3-rich air mass in the nocturnal RL from the eastern to the western YRD. As the regional O3 transport reached the RL over the western YRD, O3 concentrations in the RL accumulated and rose to 200 µg m−3 over the western Nanjing site during the sunrise hours on 25 August. The development of the daytime convective boundary layer after sunrise resulted in the disappearance of the RL, as the vertical mixing in the convective boundary layer uniformly redistributed O3 from the upper levels via the entrainment of O3-rich RL air down to the O3-poor air at the ground. This net downward transport flux reached up to 35 µg m−3 h−1, and contributed a considerable surface O3 accumulation, resulting in severe daytime O3 pollution during the summer smog event on 25 August in the western YRD region. The mechanism of regional O3 transport through the nocturnal RL revealed in this study has great implications regarding understanding O3 pollution and air quality change.
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Wang, Fan, Guo Zhen Tan, and Chao Deng. "Parallel SMO for Traffic Flow Forecasting." Applied Mechanics and Materials 20-23 (January 2010): 843–48. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.843.
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Accurate traffic flow forecasting is crucial to the development of intelligent transportation systems and advanced traveler information systems. Since Support Vector Machine (SVM)have better generalization performance and can guarantee global minima for given training data, it is believed that SVR is an effective method in traffic flow forecasting. But with the sharp increment of traffic data, traditional serial SVM can not meet the real-time requirements of traffic flow forecasting. Parallel processing has been proved to be a good method to reduce training time. In this paper, we adopt a parallel sequential minimal optimization (Parallel SMO) method to train SVM in multiple processors. Our experimental and analytical results demonstrate this model can reduce training time, enhance speed-up ratio and efficiency and better satisfy the real-time demands of traffic flow forecasting.
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Manders, Astrid M. M., Peter J. H. Builtjes, Lyana Curier, Hugo A. C. Denier van der Gon, Carlijn Hendriks, Sander Jonkers, Richard Kranenburg, et al. "Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model." Geoscientific Model Development 10, no. 11 (November 16, 2017): 4145–73. http://dx.doi.org/10.5194/gmd-10-4145-2017.
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Abstract. The development and application of chemistry transport models has a long tradition. Within the Netherlands the LOTOS–EUROS model has been developed by a consortium of institutes, after combining its independently developed predecessors in 2005. Recently, version 2.0 of the model was released as an open-source version. This paper presents the curriculum vitae of the model system, describing the model's history, model philosophy, basic features and a validation with EMEP stations for the new benchmark year 2012, and presents cases with the model's most recent and key developments. By setting the model developments in context and providing an outlook for directions for further development, the paper goes beyond the common model description.With an origin in ozone and sulfur modelling for the models LOTOS and EUROS, the application areas were gradually extended with persistent organic pollutants, reactive nitrogen, and primary and secondary particulate matter. After the combination of the models to LOTOS–EUROS in 2005, the model was further developed to include new source parametrizations (e.g. road resuspension, desert dust, wildfires), applied for operational smog forecasts in the Netherlands and Europe, and has been used for emission scenarios, source apportionment, and long-term hindcast and climate change scenarios. LOTOS–EUROS has been a front-runner in data assimilation of ground-based and satellite observations and has participated in many model intercomparison studies. The model is no longer confined to applications over Europe but is also applied to other regions of the world, e.g. China. The increasing interaction with emission experts has also contributed to the improvement of the model's performance. The philosophy for model development has always been to use knowledge that is state of the art and proven, to keep a good balance in the level of detail of process description and accuracy of input and output, and to keep a good record on the effect of model changes using benchmarking and validation. The performance of v2.0 with respect to EMEP observations is good, with spatial correlations around 0.8 or higher for concentrations and wet deposition. Temporal correlations are around 0.5 or higher. Recent innovative applications include source apportionment and data assimilation, particle number modelling, and energy transition scenarios including corresponding land use changes as well as Saharan dust forecasting. Future developments would enable more flexibility with respect to model horizontal and vertical resolution and further detailing of model input data. This includes the use of different sources of land use characterization (roughness length and vegetation), detailing of emissions in space and time, and efficient coupling to meteorology from different meteorological models.
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Richter, Friedrich, Matthias Drusch, Lars Kaleschke, Nina Maaß, Xiangshan Tian-Kunze, and Susanne Mecklenburg. "Arctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer models." Cryosphere 12, no. 3 (March 14, 2018): 921–33. http://dx.doi.org/10.5194/tc-12-921-2018.
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Abstract. Sea ice is a crucial component for short-, medium- and long-term numerical weather predictions. Most importantly, changes of sea ice coverage and areas covered by thin sea ice have a large impact on heat fluxes between the ocean and the atmosphere. L-band brightness temperatures from ESA's Earth Explorer SMOS (Soil Moisture and Ocean Salinity) have been proven to be a valuable tool to derive thin sea ice thickness. These retrieved estimates were already successfully assimilated in forecasting models to constrain the ice analysis, leading to more accurate initial conditions and subsequently more accurate forecasts. However, the brightness temperature measurements can potentially be assimilated directly in forecasting systems, reducing the data latency and providing a more consistent first guess. As a first step towards such a data assimilation system we studied the forward operator that translates geophysical parameters provided by a model into brightness temperatures. We use two different radiative transfer models to generate top of atmosphere brightness temperatures based on ORAP5 model output for the 2012/2013 winter season. The simulations are then compared against actual SMOS measurements. The results indicate that both models are able to capture the general variability of measured brightness temperatures over sea ice. The simulated brightness temperatures are dominated by sea ice coverage and thickness changes are most pronounced in the marginal ice zone where new sea ice is formed. There we observe the largest differences of more than 20 K over sea ice between simulated and observed brightness temperatures. We conclude that the assimilation of SMOS brightness temperatures yields high potential for forecasting models to correct for uncertainties in thin sea ice areas and suggest that information on sea ice fractional coverage from higher-frequency brightness temperatures should be used simultaneously.
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Munoz Sabater, Joaquín, Anne Fouilloux, and Patricia de Rosnay. "Technical Implementation of SMOS Data in the ECMWF Integrated Forecasting System." IEEE Geoscience and Remote Sensing Letters 9, no. 2 (March 2012): 252–56. http://dx.doi.org/10.1109/lgrs.2011.2164777.
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Muñoz‐Sabater, J., H. Lawrence, C. Albergel, P. Rosnay, L. Isaksen, S. Mecklenburg, Y. Kerr, and M. Drusch. "Assimilation of SMOS brightness temperatures in the ECMWF Integrated Forecasting System." Quarterly Journal of the Royal Meteorological Society 145, no. 723 (July 2019): 2524–48. http://dx.doi.org/10.1002/qj.3577.
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Khodayar, Samiro, Amparo Coll, and Ernesto Lopez-Baeza. "An improved perspective in the spatial representation of soil moisture: potential added value of SMOS disaggregated 1 km resolution “all weather” product." Hydrology and Earth System Sciences 23, no. 1 (January 17, 2019): 255–75. http://dx.doi.org/10.5194/hess-23-255-2019.
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Abstract. This study uses the synergy of multi-resolution soil moisture (SM) satellite estimates from the Soil Moisture Ocean Salinity (SMOS) mission, a dense network of ground-based SM measurements, and a soil–vegetation–atmosphere transfer (SVAT) model, SURFEX (externalized surface), module ISBA (interactions between soil, biosphere and atmosphere), to examine the benefits of the SMOS level 4 (SMOS-L4) version 3.0, or “all weather” high-resolution soil moisture disaggregated product (SMOS-L43.0; ∼1 km). The added value compared to SMOS level 3 (SMOS-L3; ∼25 km) and SMOS level 2 (SMOS-L2; ∼15 km) is investigated. In situ SM observations over the Valencia anchor station (VAS; SMOS calibration and validation – Cal/Val – site in Europe) are used for comparison. The SURFEX (ISBA) model is used to simulate point-scale surface SM (SSM) and, in combination with high-quality atmospheric information data, namely from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Système d'analyse fournissant des renseignements atmosphériques à la neige (SAFRAN) meteorological analysis system, to obtain a representative SSM mapping over the VAS. The sensitivity to realistic initialization with SMOS-L43.0 is assessed to simulate the spatial and temporal distribution of SSM. Results demonstrate the following: (a) All SMOS products correctly capture the temporal patterns, but the spatial patterns are not accurately reproduced by the coarser resolutions, probably in relation to the contrast with point-scale in situ measurements. (b) The potential of the SMOS-L43.0 product is pointed out to adequately characterize SM spatio-temporal variability, reflecting patterns consistent with intensive point-scale SSM samples on a daily timescale. The restricted temporal availability of this product dictated by the revisit period of the SMOS satellite compromises the averaged SSM representation for longer periods than a day. (c) A seasonal analysis points out improved consistency during December–January–February and September–October–November, in contrast to significantly worse correlations in March–April–May (in relation to the growing vegetation) and June–July–August (in relation to low SSM values < 0.1 m3 m−3 and low spatial variability). (d) The combined use of the SURFEX (ISBA) SVAT model with the SAFRAN system, initialized with SMOS-L43.0 1 km disaggregated data, is proven to be a suitable tool for producing regional SM maps with high accuracy, which could be used as initial conditions for model simulations, flood forecasting, crop monitoring and crop development strategies, among others.
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Zhuo, Lu, and Dawei Han. "Hydrological Evaluation of Satellite Soil Moisture Data in Two Basins of Different Climate and Vegetation Density Conditions." Advances in Meteorology 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/1086456.
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Accurate soil moisture information is very important for real-time flood forecasting. Although satellite soil moisture observations are useful information, their validations are generally hindered by the large spatial difference with the point-based measurements, and hence they cannot be directly applied in hydrological modelling. This study adopts a widely applied operational hydrological model Xinanjiang (XAJ) as a hydrological validation tool. Two widely used microwave sensors (SMOS and AMSR-E) are evaluated, over two basins (French Broad and Pontiac) with different climate types and vegetation covers. The results demonstrate SMOS outperforms AMSR-E in the Pontiac basin (cropland), while both products perform poorly in the French Broad basin (forest). The MODIS NDVI thresholds of 0.81 and 0.64 (for cropland and forest basins, resp.) are very effective in dividing soil moisture datasets into “denser” and “thinner” vegetation periods. As a result, in the cropland, the statistical performance is further improved for both satellites (i.e., improved to NSE = 0.74, RMSE = 0.0059 m and NSE = 0.58, RMSE = 0.0066 m for SMOS and AMER-E, resp.). The overall assessment suggests that SMOS is of reasonable quality in estimating basin-scale soil moisture at moderate-vegetated areas, and NDVI is a useful indicator for further improving the performance.
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Xie, Jiping, François Counillon, Laurent Bertino, Xiangshan Tian-Kunze, and Lars Kaleschke. "Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system." Cryosphere 10, no. 6 (November 16, 2016): 2745–61. http://dx.doi.org/10.5194/tc-10-2745-2016.
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Abstract. An observation product for thin sea ice thickness (SMOS-Ice) is derived from the brightness temperature data of the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission. This product is available in near-real time, at daily frequency, during the cold season. In this study, we investigate the benefit of assimilating SMOS-Ice into the TOPAZ coupled ocean and sea ice forecasting system, which is the Arctic component of the Copernicus marine environment monitoring services. The TOPAZ system assimilates sea surface temperature (SST), altimetry data, temperature and salinity profiles, ice concentration, and ice drift with the ensemble Kalman filter (EnKF). The conditions for assimilation of sea ice thickness thinner than 0.4 m are favorable, as observations are reliable below this threshold and their probability distribution is comparable to that of the model. Two parallel Observing System Experiments (OSE) have been performed in March and November 2014, in which the thicknesses from SMOS-Ice (thinner than 0.4 m) are assimilated in addition to the standard observational data sets. It is found that the root mean square difference (RMSD) of thin sea ice thickness is reduced by 11 % in March and 22 % in November compared to the daily thin ice thicknesses of SMOS-Ice, which suggests that SMOS-Ice has a larger impact during the beginning of the cold season. Validation against independent observations of ice thickness from buoys and ice draft from moorings indicates that there are no degradations in the pack ice but there are some improvements near the ice edge close to where the SMOS-Ice has been assimilated. Assimilation of SMOS-Ice yields a slight improvement for ice concentration and degrades neither SST nor sea level anomaly. Analysis of the degrees of freedom for signal (DFS) indicates that the SMOS-Ice has a comparatively small impact but it has a significant contribution in constraining the system (> 20 % of the impact of all ice and ocean observations) near the ice edge. The areas of largest impact are the Kara Sea, Canadian Archipelago, Baffin Bay, Beaufort Sea and Greenland Sea. This study suggests that the SMOS-Ice is a good complementary data set that can be safely included in the TOPAZ system.
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Tranchant, B., C. E. Testut, N. Ferry, L. Renault, E. Obligis, C. Boone, and G. Larnicol. "Data assimilation of simulated SSS SMOS products in an ocean forecasting system." Journal of Operational Oceanography 1, no. 2 (January 1, 2008): 19–27. http://dx.doi.org/10.1080/1755876x.2008.11020099.
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El Khalki, El Mahdi, Yves Tramblay, Christian Massari, Luca Brocca, Vincent Simonneaux, Simon Gascoin, and Mohamed El Mehdi Saidi. "Challenges in flood modeling over data-scarce regions: how to exploit globally available soil moisture products to estimate antecedent soil wetness conditions in Morocco." Natural Hazards and Earth System Sciences 20, no. 10 (October 5, 2020): 2591–607. http://dx.doi.org/10.5194/nhess-20-2591-2020.
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Abstract. The Mediterranean region is characterized by intense rainfall events giving rise to devastating floods. In Maghreb countries such as Morocco, there is a strong need for forecasting systems to reduce the impacts of floods. The development of such a system in the case of ungauged catchments is complicated, but remote-sensing products could overcome the lack of in situ measurements. The soil moisture content can strongly modulate the magnitude of flood events and consequently is a crucial parameter to take into account for flood modeling. In this study, different soil moisture products (European Space Agency Climate Change Initiative, ESA-CCI; Soil Moisture and Ocean Salinity, SMOS; Soil Moisture and Ocean Salinity by the Institut National de la Recherche Agronomique and Centre d'Etudes Spatiales de la Biosphère, SMOS-IC; Advanced Scatterometer, ASCAT; and ERA5 reanalysis) are compared to in situ measurements and one continuous soil-moisture-accounting (SMA) model for basins located in the High Atlas Mountains, upstream of the city of Marrakech. The results show that the SMOS-IC satellite product and the ERA5 reanalysis are best correlated with observed soil moisture and with the SMA model outputs. The different soil moisture datasets were also compared to estimate the initial soil moisture condition for an event-based hydrological model based on the Soil Conservation Service curve number (SCS-CN). The ASCAT, SMOS-IC, and ERA5 products performed equally well in validation to simulate floods, outperforming daily in situ soil moisture measurements that may not be representative of the whole catchment soil moisture conditions. The results also indicated that the daily time step may not fully represent the saturation state before a flood event due to the rapid decay of soil moisture after rainfall in these semiarid environments. Indeed, at the hourly time step, ERA5 and in situ measurements were found to better represent the initial soil moisture conditions of the SCS-CN model by comparison with the daily time step. The results of this work could be used to implement efficient flood modeling and forecasting systems in semiarid regions where soil moisture measurements are lacking.
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Weston, Peter, and Patricia de Rosnay. "SMOS Brightness Temperature Monitoring Quality Control Review and Enhancements." Remote Sensing 13, no. 20 (October 13, 2021): 4081. http://dx.doi.org/10.3390/rs13204081.
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Brightness temperature (Tb) observations from the European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) instrument are passively monitored in the European Centre for Medium-range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). Several quality control procedures are performed to screen out poor quality data and/or data that cannot accurately be simulated from the numerical weather prediction (NWP) model output. In this paper, these quality control procedures are reviewed, and enhancements are proposed, tested, and evaluated. The enhancements presented include improved sea ice screening, coastal and ambiguous land-ocean screening, improved radio frequency interference (RFI) screening, and increased usage of observation at the edge of the satellite swath. Each of the screening changes results in improved agreement between the observations and model equivalent values. This is an important step in advance of future experiments to test the direct assimilation of SMOS Tbs into the ECMWF land data assimilation system.
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Weston, Peter, and Patricia de Rosnay. "SMOS Brightness Temperature Monitoring Quality Control Review and Enhancements." Remote Sensing 13, no. 20 (October 13, 2021): 4081. http://dx.doi.org/10.3390/rs13204081.
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Brightness temperature (Tb) observations from the European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) instrument are passively monitored in the European Centre for Medium-range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). Several quality control procedures are performed to screen out poor quality data and/or data that cannot accurately be simulated from the numerical weather prediction (NWP) model output. In this paper, these quality control procedures are reviewed, and enhancements are proposed, tested, and evaluated. The enhancements presented include improved sea ice screening, coastal and ambiguous land-ocean screening, improved radio frequency interference (RFI) screening, and increased usage of observation at the edge of the satellite swath. Each of the screening changes results in improved agreement between the observations and model equivalent values. This is an important step in advance of future experiments to test the direct assimilation of SMOS Tbs into the ECMWF land data assimilation system.
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Jakkila, Juho, Tiia Vento, Tapani Rousi, and Bertel Vehviläinen. "SMOS soil moisture data validation in the Aurajoki watershed, Finland." Hydrology Research 45, no. 4-5 (October 17, 2013): 684–702. http://dx.doi.org/10.2166/nh.2013.234.
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Soil Moisture and Ocean Salinity (SMOS) level 2 soil moisture observations were validated by the soil moisture simulations of the Watershed Simulation and Forecasting System (WSFS) in the Aurajoki watershed located in Southwest Finland. The validation period included summer seasons 2010–2012. A new 2-layer soil moisture model was developed to simulate soil moisture in two layers, 10 cm thick surface layer and 80 cm thick sub-surface layer, due to low penetration depth of the satellite observations. Both layers of the model were divided into six classes of soil types to characterize the soil properties of the study catchment. The soil parameters for different soil types were estimated using the soil moisture observation network and other model parameters were calibrated against discharge observations. The WSFS with the 2-layer soil moisture model was found appropriate in reproducing observed discharges in the test site. Despite slight dry bias, the SMOS observations were consistent with the simulated surface soil moisture content, giving Nash–Sutcliffe efficiency coefficient of 0.77 and root-mean-square deviation of 0.076. The analysis of the SMOS level 2 data presents promising results for the satellite observations to be used as an indicator of the filling of the soil moisture storage in the WSFS.
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Lin, Liao-Fan, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras. "Combined Assimilation of Satellite Precipitation and Soil Moisture: A Case Study Using TRMM and SMOS Data." Monthly Weather Review 145, no. 12 (December 2017): 4997–5014. http://dx.doi.org/10.1175/mwr-d-17-0125.1.
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This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).
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Rodríguez-Fernández, Nemesio, Patricia de Rosnay, Clement Albergel, Philippe Richaume, Filipe Aires, Catherine Prigent, and Yann Kerr. "SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact." Remote Sensing 11, no. 11 (June 3, 2019): 1334. http://dx.doi.org/10.3390/rs11111334.
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The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised-Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if local biases can remain. Experiments performing joint data assimilation (DA) of NNSM, 2 m air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April–September, while NNSM alone has a significant positive effect in July–September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 h lead time.
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Tranchant, Benoît, Elisabeth Remy, Eric Greiner, and Olivier Legalloudec. "Data assimilation of Soil Moisture and Ocean Salinity (SMOS) observations into the Mercator Ocean operational system: focus on the El Niño 2015 event." Ocean Science 15, no. 3 (May 22, 2019): 543–63. http://dx.doi.org/10.5194/os-15-543-2019.
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Abstract. Monitoring sea surface salinity (SSS) is important for understanding and forecasting the ocean circulation. It is even crucial in the context of the intensification of the water cycle. Until recently, SSS was one of the less observed essential ocean variables. Only sparse in situ observations, mostly closer to 5 m depth than the surface, were available to estimate the SSS. The recent satellite ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius SAC-D and Soil Moisture Active Passive (SMAP) missions have made it possible for the first time to measure SSS from space and can bring a valuable additional constraint to control the model salinity. Nevertheless, satellite SSS still contains some residual biases that must be removed prior to bias correction and data assimilation. One of the major challenges of this study is to estimate the SSS bias and a suitable observation error for the data assimilation system. It was made possible by modifying a 3D-Var bias correction scheme and by using the analysis of the residuals and errors with an adapted statistical technique. This article presents the design and the analysis of an observing system experiment (OSE) conducted with the 0.25∘ resolution Mercator Ocean global analysis and forecasting system during the El Niño 2015/16 event. The SSS data assimilation constrains the model to be closer to the near-surface salinity observations in a coherent way with the other data sets already routinely assimilated in an operational context. This also shows that the overestimation of E–P is corrected by data assimilation through salting in regions where precipitations are higher. Globally, the SMOS SSS assimilation has a positive impact in salinity over the top 30 m. Comparisons to independent salinity data sets show a small but positive impact and corroborate the fact that the impact of SMOS SSS assimilation is larger in the Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ) regions. There is little impact on the sea surface temperature (SST) and sea surface height (SSH) error statistics. Nevertheless, the SSH seems to be impacted by the tropical instability wave (TIW) propagation, itself linked to changes in barrier layer thickness (BLT). Finally, this study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS data assimilation performance.
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Mäkynen, Marko, Jari Haapala, Giuseppe Aulicino, Beena Balan-Sarojini, Magdalena Balmaseda, Alexandru Gegiuc, Fanny Girard-Ardhuin, et al. "Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme." Remote Sensing 12, no. 7 (April 10, 2020): 1214. http://dx.doi.org/10.3390/rs12071214.
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The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.
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Jamei, Mozhdeh, Mohammad Mousavi Baygi, Ebrahim Asadi Oskouei, and Ernesto Lopez-Baeza. "Validation of the SMOS Level 1C Brightness Temperature and Level 2 Soil Moisture Data over the West and Southwest of Iran." Remote Sensing 12, no. 17 (August 31, 2020): 2819. http://dx.doi.org/10.3390/rs12172819.
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The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission with the MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) L-band radiometer provides global soil moisture (SM) data. SM data and products from remote sensing are relatively new, but they are providing significant observations for weather forecasting, water resources management, agriculture, land surface, and climate models assessment, etc. However, the accuracy of satellite measurements is still subject to error from the retrieval algorithms and vegetation cover. Therefore, the validation of satellite measurements is crucial to understand the quality of retrieval products. The objectives of this study, precisely framed within this mission, are (i) validation of the SMOS Level 1C Brightness Temperature (TBSMOS) products in comparison with simulated products from the L-MEB model (TBL-MEB) and (ii) validation of the SMOS Level 2 SM (SMSMOS) products against ground-based measurements at 10 significant Iranian agrometeorological stations. The validations were performed for the period of January 2012 to May 2015 over the Southwest and West of Iran. The results of the validation analysis showed an RMSE ranging between 9 to 13 K and a strong correlation (R = 0.61–0.84) between TBSMOS and TBL-MEB at all stations. The bias values (0.1 to 7.5 K) showed a slight overestimation for TBSMOS at most of the stations. The results of SMSMOS validation indicated a high agreement (RMSE = 0.046–0.079 m3 m−3 and R = 0.65–0.84) between the satellite SM and in situ measurements over all the stations. The findings of this research indicated that SMSMOS shows high accuracy and agreement with in situ measurements which validate its potential. Due to the limitation of SM measurements in Iran, the SMOS products can be used in different scientific and practical applications at different Iranian study areas.
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Martin, Matthew J., Robert R. King, James While, and Ana Barbosa Aguiar. "Assimilating satellite sea‐surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system." Quarterly Journal of the Royal Meteorological Society 145, no. 719 (January 2019): 705–26. http://dx.doi.org/10.1002/qj.3461.
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Albergel, C., G. Balsamo, P. de Rosnay, J. Muñoz-Sabater, and S. Boussetta. "A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data." Hydrology and Earth System Sciences 16, no. 10 (October 16, 2012): 3607–20. http://dx.doi.org/10.5194/hess-16-3607-2012.
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Abstract. In situ soil moisture data from 122 stations across the United States are used to evaluate the impact of a new bare ground evaporation formulation at ECMWF. In November 2010, the bare ground evaporation used in ECMWF's operational Integrated Forecasting System (IFS) was enhanced by adopting a lower stress threshold than for the vegetation, allowing a higher evaporation. It results in more realistic soil moisture values when compared to in situ data, particularly over dry areas. Use was made of the operational IFS and offline experiments for the evaluation. The latter are based on a fixed version of the IFS and make it possible to assess the impact of a single modification, while the operational analysis is based on a continuous effort to improve the analysis and modelling systems, resulting in frequent updates (a few times a year). Considering the field sites with a fraction of bare ground greater than 0.2, the root mean square difference (RMSD) of soil moisture is shown to decrease from 0.118 m3 m−3 to 0.087 m3 m−3 when using the new formulation in offline experiments, and from 0.110 m3 m−3 to 0.088 m3 m−3 in operations. It also improves correlations. Additionally, the impact of the new formulation on the terrestrial microwave emission at a global scale is investigated. Realistic and dynamically consistent fields of brightness temperature as a function of the land surface conditions are required for the assimilation of the SMOS data. Brightness temperature simulated from surface fields from two offline experiments with the Community Microwave Emission Modelling (CMEM) platform present monthly mean differences up to 7 K. Offline experiments with the new formulation present drier soil moisture, hence simulated brightness temperature with its surface fields are larger. They are also closer to SMOS remotely sensed brightness temperature.
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Albergel, C., G. Balsamo, P. de Rosnay, J. Muñoz-Sabater, and S. Boussetta. "A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data." Hydrology and Earth System Sciences Discussions 9, no. 5 (May 30, 2012): 6715–52. http://dx.doi.org/10.5194/hessd-9-6715-2012.
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Abstract. In situ soil moisture data from 122 stations across the United States are used to evaluate the impact of a new bare ground evaporation formulation at ECMWF. In November 2010 the bare ground evaporation used in ECMWF's operational Integrated Forecasting System (IFS) was enhanced by adopting a lower stress threshold than for the vegetation, allowing a higher evaporation. It results in more realistic soil moisture values when compared to in situ data, particularly over dry areas. Use was made of the operational IFS and offline experiments for the evaluation. The latter are based on a fixed version of the IFS and make it possible to assess the impact of a single modification while the operational analysis is based on a continuous effort to improve the analysis and modelling systems, resulting in frequent updates (few times a year). Considering the field sites with a fraction of bare ground greater than 0.2, the root mean square difference (RMSD) of soil moisture is shown to decrease from 0.118 m3 m−3 to 0.087 m3 m−3 when using the new formulation in offline experiments, and from 0.110 m3 m−3 to 0.088 m3 m−3 in operations. It also improves correlations. Additionally the impact of the new formulation on the terrestrial microwave emission at a global scale is investigated. Realistic and dynamically consistent fields of brightness temperature as a function of the land surface conditions are required for the assimilation of the SMOS data. Brightness temperature simulated from surface fields from two offline experiments with the Community Microwave Emission Modelling (CMEM) platform present monthly mean differences up to 7 K. Offline experiment with the new formulation presents drier soil moisture, hence simulated brightness temperature with its surface fields are larger. They are also closer to SMOS remotely sensed brightness temperature.
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Sallila, Heidi, Sinéad Louise Farrell, Joshua McCurry, and Eero Rinne. "Assessment of contemporary satellite sea ice thickness products for Arctic sea ice." Cryosphere 13, no. 4 (April 12, 2019): 1187–213. http://dx.doi.org/10.5194/tc-13-1187-2019.
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Abstract. Advances in remote sensing of sea ice over the past two decades have resulted in a wide variety of satellite-derived sea ice thickness data products becoming publicly available. Selecting the most appropriate product is challenging given end user objectives range from incorporating satellite-derived thickness information in operational activities, including sea ice forecasting, routing of maritime traffic and search and rescue, to climate change analysis, longer-term modelling, prediction and future planning. Depending on the use case, selecting the most suitable satellite data product can depend on the region of interest, data latency, and whether the data are provided routinely, for example via a climate or maritime service provider. Here we examine a suite of current sea ice thickness data products, collating key details of primary interest to end users. We assess 8 years of sea ice thickness observations derived from sensors on board the CryoSat-2 (CS2), Advanced Very-High-Resolution Radiometer (AVHRR) and Soil Moisture and Ocean Salinity (SMOS) satellites. We evaluate the satellite-only observations with independent ice draft and thickness measurements obtained from the Beaufort Gyre Exploration Project (BGEP) upward looking sonar (ULS) instruments and Operation IceBridge (OIB), respectively. We find a number of key differences among data products but find that products utilizing CS2-only measurements are reliable for sea ice thickness, particularly between ∼0.5 and 4 m. Among data compared, a blended CS2-SMOS product was the most reliable for thin ice. Ice thickness distributions at the end of winter appeared realistic when compared with independent ice draft measurements, with the exception of those derived from AVHRR. There is disagreement among the products in terms of the magnitude of the mean thickness trends, especially in spring 2017. Regional comparisons reveal noticeable differences in ice thickness between products, particularly in the marginal seas in areas of considerable ship traffic.
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Wanders, N., D. Karssenberg, A. de Roo, S. M. de Jong, and M. F. P. Bierkens. "The suitability of remotely sensed soil moisture for improving operational flood forecasting." Hydrology and Earth System Sciences 18, no. 6 (June 24, 2014): 2343–57. http://dx.doi.org/10.5194/hess-18-2343-2014.
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Abstract. We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5–10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.
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Wanders, N., D. Karssenberg, A. de Roo, S. M. de Jong, and M. F. P. Bierkens. "The suitability of remotely sensed soil moisture for improving operational flood forecasting." Hydrology and Earth System Sciences Discussions 10, no. 11 (November 14, 2013): 13783–816. http://dx.doi.org/10.5194/hessd-10-13783-2013.
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Abstract. We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model for flood predictions with lead times up to 10 days. For this study, satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF, are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 5–10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more data is assimilated into the system and the best performance is achieved with the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.
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Xie, Jiping, François Counillon, and Laurent Bertino. "Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis." Cryosphere 12, no. 11 (November 26, 2018): 3671–91. http://dx.doi.org/10.5194/tc-12-3671-2018.
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Abstract. Accurately forecasting the sea-ice thickness (SIT) in the Arctic is a major challenge. The new SIT product (referred to as CS2SMOS) merges measurements from the CryoSat-2 and SMOS satellites on a weekly basis during the winter. The impact of assimilating CS2SMOS data is tested for the TOPAZ4 system – the Arctic component of the Copernicus Marine Environment Monitoring Services (CMEMS). TOPAZ4 currently assimilates a large set of ocean and sea-ice observations with the Deterministic Ensemble Kalman Filter (DEnKF). Two parallel reanalyses are conducted without (Official run) and with (Test run) assimilation of CS2SMOS data from 19 March 2014 to 31 March 2015. Since only mapping errors were provided in the CS2SMOS observation, an arbitrary term was added to compensate for the missing errors, but was found a posteriori too large. The SIT bias (too thin) is reduced from 16 to 5 cm and the standard errors decrease from 53 to 38 cm (by 28 %) when compared to the assimilated SIT. When compared to independent SIT observations, the error reduction is 24 % against the ice mass balance (IMB) buoy 2013F and by 12.5 % against SIT data from the IceBridge campaigns. The improvement of sea-ice volume persists through the summer months in the absence of CS2SMOS data. Comparisons to sea-ice drift from the satellites show that dynamical adjustments reduce the drift errors around the North Pole by about 8 %–9 % in December 2014 and February 2015. Finally, using the degrees of freedom for signal (DFS), we find that CS2SMOS makes the prime source of information in the central Arctic and in the Kara Sea. We therefore recommend the assimilation of C2SMOS for Arctic reanalyses in order to improve the ice thickness and the ice drift.
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Jadidoleslam, Navid, Ricardo Mantilla, and Witold F. Krajewski. "Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions." Hydrology 8, no. 1 (March 20, 2021): 52. http://dx.doi.org/10.3390/hydrology8010052.
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The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by the distributed hydrologic model HLM. They use SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity) data in an agricultural region of the state of Iowa in the central U.S. They explore three different strategies for updating model soil moisture states using satellite-based soil moisture observations. The first is a “hard update” method equivalent to replacing the model soil moisture with satellite observed soil moisture. The second is Ensemble Kalman Filter (EnKF) to update the model soil moisture, accounting for modeling and observational errors. The third strategy introduces a time-dependent error variance model of satellite-based soil moisture observations for perturbation of EnKF. The study compares streamflow predictions with 131 USGS gauge observations for four years (2015–2018). The results indicate that assimilating satellite-based soil moisture using EnKF reduces predicted peak error compared to that from the open-loop and hard update data assimilation. Furthermore, the inclusion of the time-dependent error variance model in EnKF improves overall streamflow prediction performance. Implications of the study are useful for the application of satellite soil moisture for operational real-time streamflow forecasting.
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Balan-Sarojini, Beena, Steffen Tietsche, Michael Mayer, Magdalena Balmaseda, Hao Zuo, Patricia de Rosnay, Tim Stockdale, and Frederic Vitart. "Year-round impact of winter sea ice thickness observations on seasonal forecasts." Cryosphere 15, no. 1 (January 26, 2021): 325–44. http://dx.doi.org/10.5194/tc-15-325-2021.
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Abstract. Nowadays many seasonal forecasting centres provide dynamical predictions of sea ice. While initializing sea ice by assimilating sea ice concentration (SIC) is common, constraining initial conditions of sea ice thickness (SIT) is only in its early stages. Here, we make use of the availability of Arctic-wide winter SIT observations covering 2011–2016 to constrain SIT in the ECMWF (European Centre for Medium-Range Weather Forecasts) ocean–sea-ice analysis system with the aim of improving the initial conditions of the coupled forecasts. The impact of the improved initialization on the predictive skill of pan-Arctic sea ice for lead times of up to 7 months is investigated in a low-resolution analogue of the currently operational ECMWF seasonal forecasting system SEAS5. By using winter SIT information merged from CS2 and SMOS (CS2SMOS: CryoSat-2 Soil Moisture and Ocean Salinity), substantial changes in sea ice volume and thickness are found in the ocean–sea-ice analysis, including damping of the overly strong seasonal cycle of sea ice volume. Compared with the reference experiment, which does not use SIT information, forecasts initialized using SIT data show a reduction of the excess sea ice bias and an overall reduction of seasonal sea ice area forecast errors of up to 5 % at lead months 2 to 5. Change in biases is the main forecast impact. Using the integrated ice edge error (IIEE) metric, we find significant improvement of up to 28 % in the September sea ice edge forecast started in April. However, sea ice forecasts for September started in spring still exhibit a positive sea ice bias, which points to a melting that is too slow in the forecast model. A slight degradation in skill is found in the early freezing season sea ice forecasts initialized in July and August, which is related to degraded initial conditions during these months. Both ocean reanalyses, with and without SIT constraint, show strong melting in the middle of the melt season compared to the forecasts. This excessive melting related to positive net surface radiation biases in the atmospheric flux forcing of the ocean reanalyses remains and consequently degrades analysed summer SIC. The impact of thickness initialization is also visible in the sea surface and near-surface temperature forecasts. While positive forecast impact is seen in near-surface temperature forecasts of early freezing season (September–October–November) initialized in May (when the sea ice initial conditions have been observationally constrained in the preceding winter months), negative impact is seen for the same season when initialized in the month of August when the sea ice initial conditions are degraded. We conclude that the strong thinning by CS2SMOS initialization mitigates or enhances seasonally dependent forecast model errors in sea ice and near-surface temperatures in all seasons. The results indicate that the memory of SIT in the spring initial conditions lasts into autumn, influencing forecasts of the peak summer melt and early freezing seasons. Our results demonstrate the usefulness of new sea ice observational products in both data assimilation and forecasting systems, and they strongly suggest that better initialization of SIT is crucial for improving seasonal sea ice forecasts.
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Liu, Yujue, Yubao Liu, Domingo Muñoz-Esparza, Fei Hu, Chao Yan, and Shiguang Miao. "Simulation of Flow Fields in Complex Terrain with WRF-LES: Sensitivity Assessment of Different PBL Treatments." Journal of Applied Meteorology and Climatology 59, no. 9 (September 1, 2020): 1481–501. http://dx.doi.org/10.1175/jamc-d-19-0304.1.
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AbstractA multiscale modeling study of a real case has been conducted to explore the capability of the large-eddy simulation version of the Weather Research and Forecasting Model (WRF-LES) over Xiaohaituo Mountain (a game zone for the Beijing, China, 2022 Winter Olympic Games). In comparing WRF-LES results with observations collected during the Mountain Terrain Atmospheric Observations and Modeling (MOUNTAOM) field campaign, it is found that at 37-m resolution with LES settings, the model can reasonably capture both large-scale events and microscale atmospheric circulation characteristics. Employing the Shuttle Radar Topography Mission 1 arc s dataset (SRTM1; ~30 m) high-resolution topographic dataset instead of the traditional USGS_30s (~900 m) dataset effectively improves the model capability for reproducing fluctuations and turbulent features of surface winds. Five sensitivity experiments are conducted to investigate the impact of different PBL treatments, including YSU/Shin and Hong (SH) PBL schemes and LES with 1.5-order turbulence kinetic energy closure model (1.5TKE), Smagorinsky (SMAG), and nonlinear backscatter and anisotropy (NBA) subgrid-scale (SGS) stress models. In this case, at gray-zone scales, differences between YSU and SH are negligible. LES outperform two PBL schemes that generate smaller turbulence kinetic energy and increase the model errors for mean wind speed, energy spectra, and probability density functions of velocity. Another key finding is that wind field features in the boundary layer over complex terrain are more sensitive to the choice of SGS models than above the boundary layer. With the increase of model resolution, the effects of the SGS model become more significant, especially for the statistical characteristics of turbulence. Among these three SGS models, NBA has the best performance. Overall, this study demonstrates that WRF-LES is a promising tool for simulating real weather flows over complex terrain.
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Krylova, Anastasiya A., Natalya G. Krylova, and Elena V. Tikhomirova. "The experience of bullying in the teacher's personal history." Yaroslavl Pedagogical Bulletin 1, no. 124 (2022): 40–47. http://dx.doi.org/10.20323/1813-145x-2022-1-124-40-47.
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In this article, the authors made an attempt to identify the specifics of attitudes towards bullying, its perception among school teachers who have experienced bullying in the past, namely, during schooling. The increasing spread of bullying in the educational environment is also associated with the inability, unwillingness of teachers to cope with this problem. The authors, drawing attention to this context, suggested that the degree of concretization of ideas, differentiation of bullying signs, understanding of the ways of constructive solution of the accompanying problems among teachers is associated with the personal experience of living in a bullying situation in the past: with the role position (aggressor / victim) and the type of bullying, with whom he met. The study used the «Smob» method (H. Kasper, 2010) to identify the type of bullying that teachers faced in personal childhood history and a semi-structured interview to determine the role position in which the person was at the time of the bullying situation in the personal history and attitudes towards this phenomenon in the present. The study involved 40 teachers (M = 48.8 years, SD = 18.3) with experience of living in a bullying situation in the past as a victim or aggressor. As a result, th e authors come to the conclusion that the position of the victim, as more traumatic, often associated with a situation of systematic, repeated bullying — type I bullying, causes difficulties in differentiating the phenomenon in the present, leads to its ignoring, fear of collision in professional activity, does not allow the teacher to adequately and respond effectively if it occurs due to the fact that he does not believe in his own strength. The position of the aggressor, more often associated with type II bullying in the case of teachers — «a separate event», less traumatic for their personality, is practically not accompanied by cognitive distortions in the notion of bullying in the present. They understand the need to prevent such situations in a child's environment, they believe in the possibility of timely forecasting and prevention.
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Ge, Lingling, Renlong Hang, Yi Liu, and Qingshan Liu. "Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations." Remote Sensing 10, no. 9 (August 21, 2018): 1327. http://dx.doi.org/10.3390/rs10091327.
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Soil moisture (SM) plays an important role in hydrological cycle and weather forecasting. Satellite provides the only viable approach to regularly observe large-scale SM dynamics. Conventionally, SM is estimated from satellite observations based on the radiative transfer theory. Recent studies have demonstrated that the neural network (NN) method can retrieve SM with comparable accuracy as conventional methods. Here, we are interested in whether the NN model with more complex structures, namely deep convolutional neural network (DCNN), can bring about further improvement in SM retrievals when compared with the NN model used in recent studies. To achieve this objective, the same input data are used for the DCNN and NN models, including L-band Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB), C-band Advanced Scatterometer (ASCAT) backscattering coefficients, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and soil temperature. The target SM used to train the DCNN and NN models is the European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim) product. The experiment consists of two phases: the learning phase from 1 January to 31 December 2015 and the testing phase from 1 January to 31 December 2016. In the learning phase, we train the DCNN and NN models using the ERA-Interim SM. When evaluation between DCNN and NN against in situ measurements in the testing phase, we find that the temporal correlations between DCNN SM and in situ measurements are higher than those between NN SM and in situ measurements by 6 . 2 % and 2 . 5 % on ascending and descending orbits, respectively. In addition, from the perspective of temporal and spatial dynamics, the simulated SM values by DCNN/NN and the ERA-Interim SM agree relatively well at a global scale. Results suggest that both NN and DCNN models are effective in estimating SM from satellite observations, and DCNN can achieve slightly better performance than NN.
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Sazib, Nazmus, Iliana Mladenova, and John Bolten. "Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data." Remote Sensing 10, no. 8 (August 11, 2018): 1265. http://dx.doi.org/10.3390/rs10081265.
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Soil moisture is considered to be a key variable to assess crop and drought conditions. However, readily available soil moisture datasets developed for monitoring agricultural drought conditions are uncommon. The aim of this work is to examine two global soil moisture datasets and a set of soil moisture web-based processing tools developed to demonstrate the value of the soil moisture data for drought monitoring and crop forecasting using the Google Earth Engine (GEE). The two global soil moisture datasets discussed in the paper are generated by integrating the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions’ satellite-derived observations into a modified two-layer Palmer model using a one-dimensional (1D) ensemble Kalman filter (EnKF) data assimilation approach. The web-based tools are designed to explore soil moisture variability as a function of land cover change and to easily estimate drought characteristics such as drought duration and intensity using soil moisture anomalies and to intercompare them against alternative drought indicators. To demonstrate the utility of these tools for agricultural drought monitoring, the soil moisture products and vegetation- and precipitation-based products were assessed over drought-prone regions in South Africa and Ethiopia. Overall, the 3-month scale Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) showed higher agreement with the root zone soil moisture anomalies. Soil moisture anomalies exhibited lower drought duration, but higher intensity compared with SPIs. Inclusion of the global soil moisture data into the GEE data catalog and the development of the web-based tools described in the paper enable a vast diversity of users to quickly and easily assess the impact of drought and improve planning related to drought risk assessment and early warning. The GEE also improves the accessibility and usability of the earth observation data and related tools by making them available to a wide range of researchers and the public. In particular, the cloud-based nature of the GEE is useful for providing access to the soil moisture data and scripts to users in developing countries that lack adequate observational soil moisture data or the necessary computational resources required to develop them.
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Shi, Jia, Pingping Xiong, Yingjie Yang, and Beichen Quan. "Forecasting smog in Beijing using a novel time-lag GM(1,N) model based on interval grey number sequences." Grey Systems: Theory and Application ahead-of-print, ahead-of-print (December 22, 2020). http://dx.doi.org/10.1108/gs-02-2020-0025.
Full textAbstract:
PurposeSmog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction.Design/methodology/approachThis paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness.FindingsIn order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies.Practical implicationsThe proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective.Originality/valueBased on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model.
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Howell, Brian, LT Sean Egan, and LT Caitlin Fine. "Application of microwave space-based environmental monitoring (SBEM) data for operational tropical cyclone intensity estimation at the Joint Typhoon Warning Center." Bulletin of the American Meteorological Society, July 15, 2022. http://dx.doi.org/10.1175/bams-d-21-0180.1.
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Abstract The Joint Typhoon Warning Center (JTWC) utilized new space-based environmental monitoring (SBEM) data alongside traditional data to adjust JTWC tropical cyclone (TC) intensity and structure estimates during production of the official 2019 Best Track dataset. Intensity estimates from multiple platforms such as Advanced Microwave Scanning Radiometer-2 (AMSR2), the Soil Moisture Active Passive (SMAP) and Soil Moisture & Ocean Salinity (SMOS) radiometers and Synthetic Aperture Radar (SAR), along with objective Dvorak and satellite consensus algorithms, not only aided the post-storm best track (BT) process, but also provided robust data that supported real-time analysis and forecasting. This summary attempts to communicate with the TC community the extent to which these new data affected the 2019 official BT data, how JTWC utilized these new data in the post-storm BT process and provide examples of how these data influenced forecaster decision making in real time. This paper makes no attempt to validate the accuracy of the wind speed estimates from these methods (SAR, SMAP/SMOS, or AMSR2) and does not outline the entirety of the JTWC process for determining TC intensity, but it does outline, briefly, the impact of these new data sets on the final JTWC BT intensity estimates and on real-time analysis. These methodologies are valuable sources of cyclone intensity estimates in an otherwise data sparse area of responsibility, and in many cases provide critical data not captured by traditional methods alone, which are detailed further in this summary.
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van Sebille, Erik, Erik Zettler, Nicolas Wienders, Linda Amaral-Zettler, Shane Elipot, and Rick Lumpkin. "Dispersion of Surface Drifters in the Tropical Atlantic." Frontiers in Marine Science 7 (January 15, 2021). http://dx.doi.org/10.3389/fmars.2020.607426.
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The Tropical Atlantic Ocean has recently been the source of enormous amounts of floating Sargassum macroalgae that have started to inundate shorelines in the Caribbean, the western coast of Africa and northern Brazil. It is still unclear, however, how the surface currents carry the Sargassum, largely restricted to the upper meter of the ocean, and whether observed surface drifter trajectories and hydrodynamical ocean models can be used to simulate its pathways. Here, we analyze a dataset of two types of surface drifters (38 in total), purposely deployed in the Tropical Atlantic Ocean in July, 2019. Twenty of the surface drifters were undrogued and reached only ∼8 cm into the water, while the other 18 were standard Surface Velocity Program (SVP) drifters that all had a drogue centered around 15 m depth. We show that the undrogued drifters separate more slowly than the drogued SVP drifters, likely because of the suppressed turbulence due to convergence in wind rows, which was stronger right at the surface than at 15 m depth. Undrogued drifters were also more likely to enter the Caribbean Sea. We also show that the novel Surface and Merged Ocean Currents (SMOC) product from the Copernicus Marine Environmental Service (CMEMS) does not clearly simulate one type of drifter better than the other, highlighting the need for further improvements in assimilated hydrodynamic models in the region, for a better understanding and forecasting of Sargassum drift in the Tropical Atlantic.