scholarly journals An Improved Method Combining CNN and 1D-Var for the Retrieval of Atmospheric Humidity Profiles from FY-4A/GIIRS Hyperspectral Data

2021 ◽  
Vol 13 (23) ◽  
pp. 4737
Author(s):  
Pengyu Huang ◽  
Qiang Guo ◽  
Changpei Han ◽  
Huangwei Tu ◽  
Chunming Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Brightness Temperature ◽  
Temporal Resolution ◽  
Reanalysis Data ◽  
Temperature Data ◽  
High Temporal Resolution ◽  
Observation Data ◽  
Retrieval Method ◽  
Humidity Profile ◽  
Humidity Profiles

FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) is the first infrared hyperspectral atmospheric vertical detector in geostationary orbit. Compared to other similar instruments, it has the advantages of high temporal resolution and stationary relative to the ground. Based on the characteristics of GIIRS observation data, we proposed a humidity profile retrieval method. We fully utilized the information provided by the observation and forecast data, and used the two-dimensional brightness temperature data with the dimension of time and optical spectrum as the input of the CNN (convolution neural network model). Then, the obtained brightness temperature data were shown to be more suitable as the input for the physical retrieval method for humidity than the conventional correction method, improving the accuracy of humidity profile retrieval. We performed two comparative experiments. The first experiment results indicate that, compared to ordinary linear correction and ANN (artificial neural network algorithm) correction, our revised observed brightness temperature data are much closer to the simulated brightness temperature obtained by inputting ERA5 reanalysis data into RTTOV (Radiative Transfer for TOVS). The results of the second experiment indicate that the accuracy of the humidity profile retrieved by our method is higher than that of conventional ANN and 1D-Var (one-dimensional variational algorithm). With ERA5 reanalysis data as the reference value, the RMSE (Root Mean Squared Error) of the humidity profiles by our method is less than 8.2% between 250 and 600 hPa. Our method holds the unique advantage of the high temporal resolution of GIIRS, improves the accuracy of humidity profile retrieval, and proves that the combination of machine learning and the physical method is a compelling idea in the field of satellite atmospheric remote sensing worthy of further exploration.

An Optimization Model for the Identification of Temperature in Intelligent Building

2011 ◽  
Vol 4 (2) ◽  
pp. 61-69 ◽  
Author(s):  
ZhenYa Zhang ◽  
HongMei Cheng ◽  
ShuGuang Zhang
Keyword(s):  
Neural Network ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Fitness Function ◽  
Temperature Data ◽  
Temperature Fields ◽  
Observation Data ◽  
Feed Forward Neural Network ◽  
Intelligent Building ◽  
Feed Forward

Methods for the reconstruction of temperature fields in an intelligent building with temperature data of discrete observation positions is a current topic of research. To reconstruct temperature field with observation data, it is necessary to model the identification of temperature in each observation position. In this paper, models for temperature identification in an intelligent building are formalized as optimization problems based on observation temperature data sequence. To solve the optimization problem, a feed forward neural network is used to formalize the identification structure, and connection matrixes of the neural network are the identification parameters. With the object function for the given optimization problem as the fitness function, the training of the feed forward neural network is driven by a genetic algorithm. The experiment for the precision and stability of the proposed method is designed with real temperature data from an intelligent building.

scholarly journals The Estimation of Surface Albedo from DSCOVR EPIC

2020 ◽  
Vol 12 (11) ◽  
pp. 1897
Author(s):  
Qiuyue Tian ◽  
Qiang Liu ◽  
Jie Guang ◽  
Leiku Yang ◽  
Hanwei Zhang ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Surface Albedo ◽  
Climate Models ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
High Temporal Resolution ◽  
Observation Data ◽  
Climate Change Research ◽  
Narrow Channels ◽  
The Earth

Surface albedo is an important parameter in climate models. The main way to obtain continuous surface albedo for large areas is satellite remote sensing. However, the existing albedo products rarely meet daily-scale requirements, which has a large impact on climate change research and rapid dynamic changes of surface analysis. The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) platform, which was launched into the Sun–Earth’s first Lagrange Point (L1) orbit, can provide spectral images of the entire sunlit face of Earth with 10 narrow channels (from 317 to 780 nm). As EPIC can provide high-temporal resolution data, it is beneficial to explore the feasibility of EPIC to estimate high-temporal resolution surface albedo. In this study, hourly surface albedo was calculated based on EPIC observation data. Then, the estimated albedo results were validated by ground-based observations of different land cover types. The results show that the EPIC albedo is basically consistent with the trend of the ground-based observations in the whole time series variation. The diurnal variation of the surface albedo from the hourly EPIC albedo exhibits a “U” shape curve, which has the same trend as the ground-based observations. Therefore, EPIC is helpful to enhance the temporal resolution of surface albedo to diurnal. Surfaces with a three-dimensional structure that casts shadows display the hotspot effect, producing a reflectance peak in the retro-solar direction and lower reflectance at viewing angles away from the solar direction. DSCOVR observes the entire sunlit face of the Earth, which is helpful to make up for the deficiency in the observations of traditional satellites in the hotspot direction in bidirectional reflectance distribution function (BRDF) research, and can help to improve the underestimation of albedo in the direction of hotspot observation.

Water vapour trends in Switzerland from radiometry, FTIR and GNSS ground stations

2020 ◽  
Author(s):  
Leonie Bernet ◽  
Elmar Brockmann ◽  
Thomas von Clarmann ◽  
Niklaus Kämpfer ◽  
Emmanuel Mahieu ◽  
...  
Keyword(s):  
Water Vapour ◽  
Temporal Resolution ◽  
Regional Climate ◽  
Satellite System ◽  
Reanalysis Data ◽  
High Temporal Resolution ◽  
Integrated Water Vapour ◽  
Global Navigation Satellite ◽  
Increasing Temperature ◽  
Gnss Data

<div> <div>Water vapour in the atmosphere is not only a strong greenhouse gas, but also affects many atmospheric processes such as the formation of clouds and precipitation. With increasing temperature, Integrated Water Vapour (IWV) is expected to increase. Analysing how atmospheric water vapour changes in time is therefore important to monitor ongoing climate change. To determine whether IWV increases in Switzerland as expected, we asses IWV trends from a tropospheric water radiometer (TROWARA) in Bern, from a Fourier transform infrared (FTIR) spectrometer at Jungfraujoch and from the Swiss network of ground-based Global Navigation Satellite System (GNSS) stations. In addition, trends are assessed from reanalysis data, using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) and the Modern-Era Retrospecitve Analysis for Research and Applications (MERRA-2).</div> <div>Ground-based GNSS data are well suited for IWV trends due to their high temporal resolution and the spatially dense networks. However, they are highly sensitvie to instrumental changes and care has to be taken when determining GNSS based trends. We therefore use a straightforward trend method to account for jumps in the GNSS data when instrumental changes were performed.</div> <div>Our data show mostly positive IWV trends between 2 and 5% per decade in Switzerland. GNSS trends are significant for some stations and the significance has the tendency to increase with altitude. Further, we found that IWV scales on average to lower tropospheric temperatures as expected, except in winter. However, the correlation between IWV and temperature based on reanalysis data is spatially incoherent. Besides our positive IWV trends, we found a good agreement of radiometer, GNSS and reanalysis data in Bern. Further, we found a dry bias of the FTIR compared to GNSS data at Jungfraujoch, due to the restriction of FTIR to clear-sky conditions. Our results are generally consistent with the positive water vapour feedback in a warming climate. We show that ground-based GNSS networks provide a valuable source for regional climate monitoring with high spatial and temporal resolution, but homogeneously reprocessed data and advanced trend techniques are needed to account for data jumps.</div> </div>

scholarly journals An Improved Method Combining ANN and 1D-Var for the Retrieval of Atmospheric Temperature Profiles from FY-4A/GIIRS Hyperspectral Data

2021 ◽  
Vol 13 (3) ◽  
pp. 481
Author(s):  
Pengyu Huang ◽  
Qiang Guo ◽  
Changpei Han ◽  
Chunming Zhang ◽  
Tianhang Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Satellite Observation ◽  
Hyperspectral Data ◽  
Radiative Transfer Model ◽  
Average Error ◽  
Transfer Model ◽  
Observation Data ◽  
Temperature Profiles ◽  
Retrieval Method ◽  
The Impact

In our study, a retrieval method of temperature profiles is proposed which combines an improved one-dimensional variational algorithm (1D-Var) and artificial neural network algorithm (ANN), using FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) infrared hyperspectral data. First, according to the characteristics of the FY-4A/GIIRS observation data using the conventional 1D-Var, we introduced channel blacklists and discarded the channels that have a large negative impact on retrieval, then used the information capacity method for channel selection and introduced a neural network to correct the satellite observation data. The improved 1D-Var effectively used the observation information of 1415 channels, reducing the impact of the error of the satellite observation and radiative transfer model, and realizing the improvement of retrieval accuracy. We subsequently used the improved 1D-Var and ANN algorithms to retrieve the temperature profiles, respectively, from the GIIRS data. The results showed that the accuracy when using ANN is better than using improved 1D-Var in situations where the pressure ranges from 800 hPa to 1000 hPa. Therefore, we combined the improved 1D-Var and ANN method to retrieve temperature profiles for different pressure levels, calculating the error by taking sounding data published by the University of Wyoming as the true values. The results show that the average error of the retrieved temperature profiles is smaller than 2 K when using our method, this method makes the accuracy of the retrieved temperature profiles superior to the accuracy of the GIIRS products from 10 hPa to 575 hPa. All in all, through the combination of the physical retrieval method and the machine learning retrieval method, this paper can certainly provide a reference for improving the accuracy of products.

scholarly journals Spatiotemporal Fusion of Remote Sensing Image Based on Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiaofei Wang ◽  
Xiaoyi Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Temporal Resolution ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Series Data ◽  
High Temporal Resolution ◽  
Sensing Data ◽  
Modis Data ◽  
Residual Image

High spatial and temporal resolution remote sensing data play an important role in monitoring the rapid change of the earth surface. However, there is an irreconcilable contradiction between the spatial and temporal resolutions of the remote sensing image acquired from a same sensor. The spatiotemporal fusion technology for remote sensing data is an effective way to solve the contradiction. In this paper, we will study the spatiotemporal fusion method based on the convolutional neural network, which can fuse the Landsat data with high spatial but low temporal resolution and MODIS data with low spatial but high temporal resolution, and generate time series data with high spatial resolution. In order to improve the accuracy of spatiotemporal fusion, a residual convolution neural network is proposed. MODIS image is used as the input to predict the residual image between MODIS and Landsat, and the sum of the predicted residual image and MODIS data is used as the predicted Landsat-like image. In this paper, the residual network not only increases the depth of the superresolution network but also avoids the problem of vanishing gradient due to the deep network structure. The experimental results show that the prediction accuracy by our method is greater than that of several mainstream methods.

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

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.

Assessment of atmospheric conditions over the Hong Thai Binh river watershed by means of dynamically downscaled ERA-20C reanalysis data

2018 ◽  
Vol 11 (2) ◽  
pp. 540-555 ◽  
Author(s):  
C. Ho ◽  
A. Nguyen ◽  
A. Ercan ◽  
M. L. Kavvas ◽  
V. Nguyen ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Wrf Model ◽  
Reanalysis Data ◽  
Observation Data ◽  
Human Society ◽  
Atmospheric Conditions ◽  
Study Region ◽  
River Watershed ◽  
Environmentally Sustainable ◽  
Atmospheric Data

Abstract Long-term, high spatial and temporal resolution of atmospheric data is crucial for the purpose of reducing the effects of hydro-meteorological risks on human society in an economically and environmentally sustainable manner. However, such information usually is limited in transboundary regions due to different governmental policies, and to conflicts in the sharing of data. In this study, high spatial and temporal resolution atmospheric data were reconstructed by means of the Weather Research and Forecasting Model-WRF with input provided from the global atmospheric reanalysis of the 20th century (ERA-20C) over the Hong-Thai Binh River watershed (H-TBRW). The WRF model was implemented over the physical boundaries of the study region based on ERA-20C reanalysis data and was configured based on existing ground observation data in Vietnam's territories, and the global Aphrodite precipitation data. With the validated WRF model for H-TBRW, the reconstructed atmospheric data were first reconstructed for 1950–2010, and then were evaluated by time series and spatial analysis methods. The results of this study suggest no significant trend in the annual accumulated precipitation depth, while there were upward trends in annual temperature at both the point and watershed scale. Furthermore, the results confirm that topographic conditions have significant effects on the climatic system such as on precipitation and temperature.

scholarly journals A More Accurate Field-to-Field Method towards the Wind Retrieval of HY-2B Scatterometer

2021 ◽  
Vol 13 (12) ◽  
pp. 2419
Author(s):  
Xinjie Shi ◽  
Boheng Duan ◽  
Kaijun Ren
Keyword(s):  
Neural Network ◽  
Wind Direction ◽  
Wind Field ◽  
Surface Wind ◽  
Field Method ◽  
Observation Data ◽  
Retrieval Method ◽  
Sea Surface Wind ◽  
The Neural Network ◽  
Mean Square Errors

In this paper, we present a method for retrieving sea surface wind field (SSWF) from HaiYang-2B (HY-2B) scatterometer data. In contrast to the conventional algorithm, i.e., using a point-to-point (P2P) method based on geophysical model functions (GMF) to retrieve SSWF by spaceborne scatterometer, we introduce a more accurate field-to-field (F2F) retrieval method based on convolutional neural network (CNN). We fully consider the spatial correlation and continuity between adjacent observation points, and input the observation data of continuous wind field within a certain range into the neural network to construct the neural network model, and then synchronously obtain the wind field within the range. The wind field obtained by our retrieval method maintains its continuity and solves the problem of ambiguity removal in traditional wind direction retrieval methods. Comparing the retrieval results with the buoy data, the results show that the root mean square errors (RMSE) of wind direction and wind speed are less than 0.18 rad (10.31°) and 0.75 m/s, respectively. The retrieval accuracy is better than the L2B product of HY-2B.

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