Study on atmospheric visibility variations and the impacts of meteorological parameters using high temporal resolution data: an application of Environmental Internet of Things in China

Over the past decade, reanalysis data products have found widespread application in many areas of research and have often been used for the assessment of the past and present climate.&#160;They produce reliable atmospheric fields at high temporal resolution, albeit at low-to-mid spatial resolution. On the other hand, climatological analyses, quite often down-scaled to represent conditions also in enclosed basins, lack the historical sequence of stormy events and are often provided at poor temporal resolution.In this context, we investigated the possibility of using the ERA5 reanalysis 10-m wind (25-km and 1-hour resolution data) to assess the Mediterranean Sea wind climate (past and scenario). We propose a statistical strategy&#160;to relate ERA5 wind speeds over the sea to the past and future wind speeds produced by the COSMO-CLM (8-km and 6-hour resolution data) climatological&#160;model. In particular, the&#160;probability density function&#160;of the&#160;ERA5&#160;wind&#160;speed&#160;at each grid point is adjusted to match that of COSMO-CLM.&#160;In this way, past ERA5 winds are corrected to account for the COSMO-CLM energy, while ERA5 scaled wind sequence can be projected in the future with COSMO-CLM scenario energy. Comparison with past observations confirms the validity of the adopted method.In the Venezia2021 project, we have applied this strategy for the assessment of the changing wind and, after WAVEWATCH III model runs, also the wave climate in the Northern Adriatic Sea, especially in front of Venice and the MOSE barriers, under two IPCC (RCP 4.5 and 8.5) scenarios.In general, this strategy may be applied to produce a scaled wind dataset in enclosed basins and improve past wave modeling applications based on any reanalysis wind data.

Download Full-text

Prediction of storm transfers and annual loads with data-based mechanistic models using high-frequency data

Hydrology and Earth System Sciences ◽

10.5194/hess-21-6425-2017 ◽

2017 ◽

Vol 21 (12) ◽

pp. 6425-6444 ◽

Cited By ~ 5

Author(s):

Mary C. Ockenden ◽

Wlodek Tych ◽

Keith J. Beven ◽

Adrian L. Collins ◽

Robert Evans ◽

...

Keyword(s):

Temporal Resolution ◽

Dynamic Models ◽

Second Order ◽

Dynamic Responses ◽

High Temporal Resolution ◽

Agricultural Catchments ◽

Antecedent Conditions ◽

Poor Water Quality ◽

The Uk ◽

Resolution Data

Abstract. Excess nutrients in surface waters, such as phosphorus (P) from agriculture, result in poor water quality, with adverse effects on ecological health and costs for remediation. However, understanding and prediction of P transfers in catchments have been limited by inadequate data and over-parameterised models with high uncertainty. We show that, with high temporal resolution data, we are able to identify simple dynamic models that capture the P load dynamics in three contrasting agricultural catchments in the UK. For a flashy catchment, a linear, second-order (two pathways) model for discharge gave high simulation efficiencies for short-term storm sequences and was useful in highlighting uncertainties in out-of-bank flows. A model with non-linear rainfall input was appropriate for predicting seasonal or annual cumulative P loads where antecedent conditions affected the catchment response. For second-order models, the time constant for the fast pathway varied between 2 and 15 h for all three catchments and for both discharge and P, confirming that high temporal resolution data are necessary to capture the dynamic responses in small catchments (10–50 km2). The models led to a better understanding of the dominant nutrient transfer modes, which will be helpful in determining phosphorus transfers following changes in precipitation patterns in the future.

Download Full-text

A high temporal resolution data set of ERS scatterometer radar backscatter for research in Arctic and sub-Arctic regions

Polar Record ◽

10.1017/s0032247400017502 ◽

2002 ◽

Vol 38 (205) ◽

pp. 115-120 ◽

Cited By ~ 4

Author(s):

Yongwei Sheng ◽

Laurence C. Smith ◽

Karen E. Frey ◽

Douglas E. Alsdorf

Keyword(s):

Temporal Resolution ◽

Incident Angle ◽

High Temporal Resolution ◽

Grid Spacing ◽

Radar Backscatter ◽

Data Set ◽

Arctic Regions ◽

Full Resolution ◽

Data Products ◽

Resolution Data

AbstractRadar backscatter in Arctic and sub-Arctic regions is temporally dynamic and reflects changes in sea ice, glacier facies, soil thaw state, vegetation cover, and moisture content. Wind scatterometers on the ERS-1 and ERS-2 satellites have amassed a global archive of C-band radar backscatter data since 1991. This paper derives three high temporal resolution data products from this archive that are designed to facilitate scatterometer research in high-latitude environments. Radar backscatter data have a grid spacing of 25 km and are mapped northwards from 60°N latitude over intervals of one, three, and seven days for the period 1991–2000. Data are corrected to a normalized incident angle of 40°. Animations and full-resolution data products are freely available for scientific use at http://merced.gis.ucla.edu/scatterometer/index.htm.

Download Full-text

A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset

Remote Sensing ◽

10.3390/rs12233888 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3888

Author(s):

Mingyuan Peng ◽

Lifu Zhang ◽

Xuejian Sun ◽

Yi Cen ◽

Xiaoyang Zhao

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Temporal Resolution ◽

Three Dimensional ◽

Remote Sensing Data ◽

High Temporal Resolution ◽

Fusion Method ◽

Sensing Data ◽

Resolution Data

With the growing development of remote sensors, huge volumes of remote sensing data are being utilized in related applications, bringing new challenges to the efficiency and capability of processing huge datasets. Spatiotemporal remote sensing data fusion can restore high spatial and high temporal resolution remote sensing data from multiple remote sensing datasets. However, the current methods require long computing times and are of low efficiency, especially the newly proposed deep learning-based methods. Here, we propose a fast three-dimensional convolutional neural network-based spatiotemporal fusion method (STF3DCNN) using a spatial-temporal-spectral dataset. This method is able to fuse low-spatial high-temporal resolution data (HTLS) and high-spatial low-temporal resolution data (HSLT) in a four-dimensional spatial-temporal-spectral dataset with increasing efficiency, while simultaneously ensuring accuracy. The method was tested using three datasets, and discussions of the network parameters were conducted. In addition, this method was compared with commonly used spatiotemporal fusion methods to verify our conclusion.

Download Full-text

Quantifying nutrient transfer pathways in agricultural catchments using high temporal resolution data

Environmental Science & Policy ◽

10.1016/j.envsci.2012.06.004 ◽

2012 ◽

Vol 24 ◽

pp. 44-57 ◽

Cited By ~ 81

Author(s):

Per-Erik Mellander ◽

Alice R. Melland ◽

Phil Jordan ◽

David P. Wall ◽

Paul N.C. Murphy ◽

...

Keyword(s):

Temporal Resolution ◽

High Temporal Resolution ◽

Nutrient Transfer ◽

Agricultural Catchments ◽

Resolution Data

Download Full-text

Methods and Challenges for Using High- Temporal Resolution Data to Monitor Urban Growth

Global Mapping of Human Settlement - Remote Sensing Applications Series ◽

10.1201/9781420083408-c16 ◽

2009 ◽

Cited By ~ 3

Author(s):

Alexandre Boucher ◽

Karen Seto

Keyword(s):

Temporal Resolution ◽

Urban Growth ◽

High Temporal Resolution ◽

Resolution Data

Download Full-text

Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

Atmospheric Measurement Techniques ◽

10.5194/amt-14-2699-2021 ◽

2021 ◽

Vol 14 (4) ◽

pp. 2699-2716

Author(s):

Yoonjin Lee ◽

Christian D. Kummerow ◽

Imme Ebert-Uphoff

Keyword(s):

Temporal Resolution ◽

Radar Data ◽

Probability Of Detection ◽

High Temporal Resolution ◽

Convective System ◽

Mountainous Regions ◽

Convective Clouds ◽

Geostationary Operational Environmental Satellite ◽

Resolution Data ◽

Infrared Data

Abstract. An ability to accurately detect convective regions is essential for initializing models for short-term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high-temporal-resolution data are mostly available over land, and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface or cloud growth that occurs over periods of 30 min to an hour. This study represents a proof of concept that artificial intelligence (AI) is able, when given high-spatial- and high-temporal-resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 µm) and 14 (11.2 µm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses a rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds and resulted in a reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

Download Full-text