scholarly journals Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

2019 ◽  
Vol 11 (7) ◽  
pp. 866 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Stefan A. Buehler
Keyword(s):  
Time Series ◽  
Radio Frequency ◽  
Time Dependent ◽  
Radio Frequency Interference ◽  
Climate Variables ◽  
Compelling Evidence ◽  
Climate Data ◽  
The Earth ◽  
Correction Scheme

Understanding the causes of inter-satellite biases in climate data records from observations of the Earth is crucial for constructing a consistent time series of the essential climate variables. In this article, we analyse the strong scan- and time-dependent biases observed for the microwave humidity sounders on board the NOAA-16 and NOAA-19 satellites. We find compelling evidence that radio frequency interference (RFI) is the cause of the biases. We also devise a correction scheme for the raw count signals for the instruments to mitigate the effect of RFI. Our results show that the RFI-corrected, recalibrated data exhibit distinctly reduced biases and provide consistent time series.

Can a statistically downscaled stochastic rainfall model conditioned on climate variables sufficiently represent future rainfall scenarios?

2020 ◽  
Author(s):  
Ross Pidoto ◽  
Uwe Haberlandt
Keyword(s):  
Time Series ◽  
Atmospheric Pressure ◽  
Fuzzy Rule ◽  
Climate Impact ◽  
Climate Variables ◽  
Climate Data ◽  
Pressure Data ◽  
Rule Based ◽  
Impact Studies ◽  
Rainfall Model

<p>Climate impact studies regarding hydrology require long precipitation time series of high spatial and temporal resolution. Global climate models (GCMs) provide global predictions of future climates, however they are a poor choice to accurately represent future surface precipitation conditions, especially at high resolutions. Instead, statistical downscaling from a GCM to a stochastic precipitation model is one common method to provide unbiased time series of arbitrary length for use within climate impact studies.</p><p>This study considers an alternating renewal stochastic rainfall model conditioned on fuzzy-rule based climate classes. The key research question for this study is whether stationarity of the climate classes can be assumed, meaning that changes to future rainfall can be explained by changes in climate class frequency alone. If stationarity of the climate classes cannot be assumed, what further steps, for example a delta-change approach, are required to adequately account for this non-stationarity.</p><p>An event based alternating renewal rainfall model has been conditioned on a fuzzy-rule based climate classification, using re-analysis climate data as input for the classification. The classification is created via an automated objective optimisation procedure that derives climate classes of non-mean (either dry or wet) rainfall behaviour.</p><p>The study area is the northern German federal state of Lower Saxony. ERA5 re-analysis climate data was used as input for the fuzzy-based classification. Previous studies using this classification method used atmospheric pressure data only, whereas this study also incorporates additional climate variables such as wind, temperature, humidity etc. 18 high-resolution rainfall gauges with a time series length of at least 15 years were used as observations for the rainfall model. A regional climate model (RCM) will be used as a reference for both past and future rainfall conditions in order to test the stated hypothesis. The climate classes derived from the re-analysis data will be reproduced for future climates using simulation results from a GCM.</p><p>Initial results indicate that the conditioning on climate classes using additional climate variables improves the single site performance of the rainfall model, particularly regarding extremes. The climate classes themselves were also shown to be more robust and diverse in terms of their rainfall behaviour when compared to classes generated from atmospheric pressure data alone. It is also hypothesised that the climate conditioned model will show improvements in predicting future precipitation conditions compared to previous studies.</p>

scholarly journals Detection and Analysis of C-Band Radio Frequency Interference in AMSR2 Data over Land

2019 ◽  
Vol 11 (10) ◽  
pp. 1228 ◽  
Author(s):  
Ying Wu ◽  
Bo Qian ◽  
Yansong Bao ◽  
Meixin Li ◽  
George P. Petropoulos ◽  
...  
Keyword(s):  
United States ◽  
Radio Frequency ◽  
Detection Method ◽  
Principal Component ◽  
The United States ◽  
Temperature Data ◽  
Radio Frequency Interference ◽  
The Earth ◽  
Pca Method ◽  
Contaminated Area

A simplified generalized radio frequency interference (RFI) detection method and principal component analysis (PCA) method are utilized to detect and attribute the sources of C-band RFI in AMSR2 L1 brightness temperature data over land during 1–16 July 2017. The results show that the consistency between the two methods provides confidence that RFI may be reliably detected using either of the methods, and the only difference is that the scope of the RFI-contaminated area identified by the former algorithm is larger in some areas than that using the latter method. Strong RFI signals at 6.925 GHz are mainly distributed in the United States, Japan, India, Brazil, and some parts of Europe; meanwhile, RFI signals at 7.3 GHz are mainly distributed in Latin America, Asia, Southern Europe, and Africa. However, no obvious 7.3 GHz RFI appears in the United States or India, indicating that the 7.3 GHz channels mitigate the effects of the C-band RFI in these regions. The RFI signals whose position does not vary with the Earth azimuth of the observations generally come from stable, continuous sources of active ground-based microwave radiation, while the RFI signals which are observed only in some directions on a kind of scanning orbit (ascending/descending) mostly arise from reflected geostationary satellite signals.

scholarly journals Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation

2021 ◽  
Vol 3 (4) ◽  
pp. 858-880
Author(s):  
Valentina Sessa ◽  
Edi Assoumou ◽  
Mireille Bossy ◽  
Sofia G. Simões
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Random Forest ◽  
Power Systems ◽  
Machine Learning Techniques ◽  
Climate Variables ◽  
Climate Data ◽  
Hydropower Generation ◽  
The Impact ◽  
Run Of River

Analyzing the impact of climate variables into the operational planning processes is essential for the robust implementation of a sustainable power system. This paper deals with the modeling of the run-of-river hydropower production based on climate variables on the European scale. A better understanding of future run-of-river generation patterns has important implications for power systems with increasing shares of solar and wind power. Run-of-river plants are less intermittent than solar or wind but also less dispatchable than dams with storage capacity. However, translating time series of climate data (precipitation and air temperature) into time series of run-of-river-based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. This task is also more complex when performed for a large interconnected area. In this work, a model is built for several European countries by using machine learning techniques. In particular, we compare the accuracy of models based on the Random Forest algorithm and show that a more accurate model is obtained when a finer spatial resolution of climate data is introduced. We then discuss the practical applicability of a machine learning model for the medium term forecasts and show that some very context specific but influential events are hard to capture.

ANALYSIS OF POTENTIAL RADIO FREQUENCY INTERFERENCE ON ANTENNA POSITIONER (3M) IN RANCABUNGUR GROUND STATION

2019 ◽  
Author(s):  
Rumadi Rumadi ◽  
◽  
Dicka Ariptian Rahayu ◽  
Nur Salma Yusuf Hasanah ◽  
Zhauhar Rainaldy Ardhana ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Interference ◽  
Ground Station

scholarly journals An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6885
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Feature Extraction ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Communication Networks ◽  
Continuous Wave ◽  
Critical Role ◽  
Video Stream ◽  
Radio Frequency Interference ◽  
Wave Interference ◽  
Continuous Wave Interference

Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

Sign in / Sign up

Export Citation Format

Share Document