scholarly journals PEMANFAATAN DATA SATELIT GMS MULTI KANAL UNTUK KEGIATAN TEKNOLOGI MODIFIKASI CUACA

2016 ◽  
Vol 17 (2) ◽  
pp. 47
Author(s):  
Muhamad Djazim Syaifullah ◽  
Satyo Nuryanto
Keyword(s):  
Grain Size ◽  
Data Management ◽  
Satellite Imagery ◽  
Satellite Data ◽  
Cloud Cover ◽  
Target Selection ◽  
Radar Data ◽  
Field Validation ◽  
Cloud Seeding ◽  
Weather Modification

IntisariTulisan ini menyajikan pemanfaatan data satelit GMS (Geostationary Meteorological Satellites) multi kanal untuk informasi perawanan dalam rangka mendukung kegiatan teknologi modifikasi cuaca. Pemanfaatan data satelit meliputi proses pengunduhan data, proses kalibrasi dan visualisasi citra satelit sehingga dapat diinterpretasi. Pemrosesan data satelit juga meliputi jenis dan tipe awan serta ukuran butir awan. Dengan diketahuinya tipe dan jenis awan maka pemilihan target awan dalam pelaksanaan Teknologi Modifikasi Cuaca (TMC) dapat lebih efektif. Data Satelit GMS yang berupa data PGM untuk berbagai kanal telah dimanfaatkan untuk analisis cuaca dan mendukung pelaksanaan kegiatan Teknologi Modifikasi Cuaca (TMC). Dari analisis beberapa kanal Infra Merah (IR) dapat diperoleh tipe/jenis awan dan ukuran butiran awan yang sangat bermanfaat untuk kepentingan Teknologi Modifikasi Cuaca. Diperlukan pengelolaan data yang lebih intensif baik manajemen data maupun kontinuitas pengunduhan data untuk menjamin kelancaran analisis. Selain itu juga diperlukan validasi lapangan misalnya dengan data radar analisis menjadi semakin akurat.  AbstractThis paper presents the utilization of GMS (Geostationary Meteorological Satellites) multichannel satellite data for cloud cover information in order to support the activities of weather modification technology or cloud seeding. These utilizations covering the process of data downloading, process calibration and visualization of satellite imagery so that it can be interpreted. Processing of satellite data also includes the type of cloud as well as cloud grain size. By knowing the type of cloud, the cloud target selection in the execution of Weather Modification Technology can be more effective. From the analysis of several Infrared (IR) channels can be obtained type/kind of cloud and grain size of the clouds that are beneficial to the interests of cloud seeding. It is required a more intensive data management and continuity of data download. It is also necessary field validation for example with radar data. The purpose of data management was the data processing became more efficient. 

scholarly journals PARAMETERISASI MODEL CUACA WRF-ARW UNTUK MENDUKUNG KEGIATAN TEKNOLOGI MODIFIKASI CUACA (TMC) DI SUMATERA, SULAWESI, DAN JAWA

2011 ◽  
Vol 12 (1) ◽  
pp. 1 ◽  
Author(s):  
Ridwan Ridwan ◽  
Mahally Kudsy
Keyword(s):  
Satellite Data ◽  
Wind Direction ◽  
Input Data ◽  
Radar Data ◽  
Global Forecast System ◽  
Cloud Seeding ◽  
Forecast System ◽  
West Sumatra ◽  
System Data ◽  
Global Data

Telah dilakukan penelitian prediksi cuaca dengan perangkat lunak model cuaca WRF-ARW selama kegiatan Teknologi Modifikasi Cuaca di Sumatera, Sulawesi, dan Jawadalam periode tahun 2010 dan 2011. Data masukan diperoleh dari prediksi global GFS(Global Forecast System) yang dapat diunduh setiap enam jam pada situs NOAA.Dengan WPS (WRF Prepossessing System), data global tersebut akan dipersempitsesuai wilayah yang akan diprediksi. Unsur cuaca yang diprediksi adalah curah hujandan arah angin yang diproses sehari sebelumnya. Hasil prediksi diolah secara spasialdengan program GrADS. Validasi dilakukan dengan mencocokan hasil GrADS dengandata radar atau data satelit. Selain itu, dilakukan juga parameterisasi untuk memperolehhasil prediksi yang lebih akurat dengan mengacu pada metode menggantikan prosesyang terlalu kecil atau kompleks secara fisik yang direpresentasikan dalam model yangdisederhanakan. Diharapkan hasil prediksi cuaca WRF-ARW ini dapat menjadi acuanuntuk menentukan peluang yang paling baik dalam periode harian untuk melakukanpenyemaian awan.Weather prediction with WRF-ARW has been carried out daily for Weather ModificationTechnology activities in Riau and West Sumatra from June 21 – July 21, 2010. The input data obtained from GFS (Global Forecast System), which can be downloaded every six hours from the NOAA website. With WPS (WRF Preprocessing System) global data will be downscaled according to the area that would be predicted. Weather components are predicted rainfall and wind direction is processed the day before. The prediction results are spatially processed with the program Grads. Validation is done by matching the results of Grads and radar data or satellite data. It is expected that the results of WRF-ARW forecast weather can be a reference to determine the best opportunities in conducting cloud seeding.

Establishing the timings of rainfall-triggered landslides using Sentinel-1 satellite radar data

2021 ◽  
Author(s):  
Katy Burrows ◽  
Odin Marc ◽  
Dominique Remy
Keyword(s):  
Time Series ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Heavy Rainfall ◽  
Hazard Mitigation ◽  
Radar Data ◽  
Mitigation Strategies ◽  
Radar Images ◽  
Satellite Radar ◽  
The Impact

<p>Heavy rainfall can trigger thousands of landslides, which have a significant effect on the landscape and can pose a hazard to people and infrastructure. Inventories of rainfall triggered landslides are used to improve our understanding of the physical mechanisms that cause the event, in assessing the impact of the event and in the development of hazard mitigation strategies. Inventories of rainfall-triggered landslides are most commonly generated using optical or multispectral satellite imagery, but such imagery is often obscured by cloud-cover associated with the rainfall event. Cloud-free optical satellite images may not be available until several weeks following an event. In the case where rain falls over a long period of time, for example during the monsoon season or successive typhoon events, the timing of the triggered landslides is usually poorly constrained. This lack of information on landslide timing limits both hazard mitigation strategies and our ability to model the physical processes behind the triggered landsliding.</p><p>Satellite radar has emerged recently as an alternative source of information on landslides. The removal of vegetation and movement of material due to a landslide alters the scattering properties of the Earth’s surface, thus giving landslides a signal in satellite radar imagery. Satellite radar data can be acquired in all weather conditions, and the regular and frequent acquisitions of the Sentinel-1 constellation, could allow landslide timing to be constrained to within a few days.  Satellite radar data has been successfully used in detecting the spatial distribution of landslides whose timing is known a-priori (for example those triggered by earthquakes). Here we demonstrate that time series of Sentinel-1 satellite radar images can also be used to achieve the opposite: the identification of landslide timing for an event whose spatial extent is known.</p><p>We analyse radar coherence and amplitude times series to identify changes in the time series associated with landslide occurrence. We compare pixels within each landslide with nearby pixels outside each landslide that have been identified to be similar in pre-rainfall Sentinel-1 and Sentinel-2 imagery. We test our methods on rainfall-triggered landslides in Nepal and Japan, both of which are mountainous countries that experience regular heavy rainfall events that are often obscured by cloud cover in optical satellite imagery.</p>

Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

2021 ◽  
Author(s):  
Brianna Pagán ◽  
Adekunle Ajayi ◽  
Mamadou Krouma ◽  
Jyotsna Budideti ◽  
Omar Tafsi
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Approaches ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Field Level ◽  
Crop Health

<p>The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level. </p><p>Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.</p><p> </p>

scholarly journals Evaluation of the Wyoming Weather Modification Pilot Project (WWMPP) Using Two Approaches: Traditional Statistics and Ensemble Modeling

2018 ◽  
Vol 57 (11) ◽  
pp. 2639-2660 ◽  
Author(s):  
Roy M. Rasmussen ◽  
Sarah A. Tessendorf ◽  
Lulin Xue ◽  
Courtney Weeks ◽  
Kyoko Ikeda ◽  
...  
Keyword(s):  
Null Hypothesis ◽  
Initial Conditions ◽  
Pilot Project ◽  
Physical Experiment ◽  
Annual Precipitation ◽  
Ensemble Modeling ◽  
Cloud Seeding ◽  
Weather Modification ◽  
Mountain Ranges ◽  
The Impact

AbstractThe Wyoming Weather Modification Pilot Project randomized cloud seeding experiment was a crossover statistical experiment conducted over two mountain ranges in eastern Wyoming and lasted for 6 years (2008–13). The goal of the experiment was to determine if cloud seeding of orographic barriers could increase snowfall and snowpack. The experimental design included triply redundant snow gauges deployed in a target–control configuration, covariate snow gauges to account for precipitation variability, and ground-based seeding with silver iodide (AgI). The outcomes of this experiment are evaluated with the statistical–physical experiment design and with ensemble modeling. The root regression ratio (RRR) applied to 118 experimental units provided insufficient statistical evidence (p value of 0.28) to reject the null hypothesis that there was no effect from ground-based cloud seeding. Ensemble modeling estimates of the impact of ground-based seeding provide an alternate evaluation of the 6-yr experiment. The results of the model ensemble approach with and without seeding estimated a mean enhancement of precipitation of 5%, with an inner-quartile range of 3%–7%. Estimating the impact on annual precipitation over these mountain ranges requires results from another study that indicated that approximately 30% of the annual precipitation results from clouds identified as seedable within the seeding experiment. Thus the seeding impact is on the order of 1.5% of the annual precipitation, compared to 1% for the statistical–physical experiment, which was not sufficient to reject the null hypothesis. These results provide an estimate of the impact of ground-based cloud seeding in the Sierra Madre and Medicine Bow Mountains in Wyoming that accounts for uncertainties in both initial conditions and model physics.

The effect of cloud cover on the development of habitat quality indices for juvenile Atlantic salmon (Salmo salar)

2003 ◽  
Vol 60 (11) ◽  
pp. 1386-1397 ◽  
Author(s):  
Philippe Girard ◽  
Daniel Boisclair ◽  
Michel Leclerc
Keyword(s):  
Grain Size ◽  
Atlantic Salmon ◽  
Water Temperature ◽  
Salmo Salar ◽  
Cloud Cover ◽  
Predictive Power ◽  
Fish Distribution ◽  
Quality Indices ◽  
High Cloud ◽  
Physical Conditions

We tested the validity of the predictions made by a habitat probabilistic index (HPI) developed using a description of the physical conditions (depth, flow velocity, grain size) used and avoided by parrs during days of different cloudiness. Thirteen surveys were designed to estimate the number and the distribution of parrs actively foraging within a 300-m reach of a river. During these surveys, the number of parrs actively foraging ranged from 12 to 118, cloud cover ranged from 5% to 100%, and water temperature ranged from 16.5 °C to 21.7 °C. The number of parrs actively foraging was negatively related to cloud cover (r2 = 0.44 to 0.88) but was independent of water temperature. HPI models developed under low (<33%) and intermediate (34–67%) cloud cover explained 82% to 98% of the local variations of fish density. The HPI model developed under high cloud cover (67–100%) was unable to predict fish distribution observed during cloudy days. Our results suggest that HPI models developed when cloudiness is >67% may have a limited predictive power.

scholarly journals Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

2019 ◽  
Author(s):  
Juan Huo ◽  
Daren Lu ◽  
Shu Duan ◽  
Yongheng Bi ◽  
Bo Liu
Keyword(s):  
Satellite Data ◽  
Radar Data ◽  
Retrieval Algorithm ◽  
Ka Band ◽  
Retrieval Accuracy ◽  
Retrieval Algorithms ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Radar Measurements ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. To better understand the accuracy of cloud top heights (CTHs) derived from passive satellite data, ground-based Ka-band radar measurements from 2016 and 2017 in Beijing were compared with CTH data inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Himawari Imager (AHI). Relative to the radar CTHs, the MODIS CTHs were found to be underestimated by −1.10 ± 2.53 km and 49 % of CTH differences were within 1.0 km. Like the MODIS results, the AHI CTHs were underestimated by −1.10 ± 2.27 km and 42 % were within 1.0 km. Both the MODIS and AHI retrieval accuracy depended strongly on the cloud depth (CD). Large differences were mainly occurring for the retrieval of thin clouds of CD  1 km, the CTH difference decreased to −0.48 ± 1.70 km for MODIS and to −0.76 ± 1.63 km for AHI. MODIS CTHs greater than 6 km showed better agreement with the radar data than those less than 4 km. Statistical analysis showed that the average AHI CTHs were lower than the average MODIS CTHs by −0.64 ± 2.36 km. The monthly accuracy of both retrieval algorithms was studied and it was found that the AHI retrieval algorithm had the largest bias in winter while the MODIS retrieval algorithm had the lowest accuracy in spring.

scholarly journals Use of satellite data for detecting icebergs and evaluating the iceberg threats

2018 ◽  
Vol 58 (4) ◽  
pp. 537-551 ◽  
Author(s):  
I. A. Bychkova ◽  
V. G. Smirnov
Keyword(s):  
Satellite Data ◽  
Radar Data ◽  
Arctic Shelf ◽  
The Arctic ◽  
Satellite Monitoring ◽  
Landsat 8 ◽  
Space Resolution ◽  
Detection Techniques ◽  
Satellite Radar ◽  
High Space

Te methods of satellite monitoring of dangerous ice formations, namely icebergs in the Arctic seas, representing a threat to the safety of navigation and economic activity on the Arctic shelf are considered. Te main objective of the research is to develop methods for detecting icebergs using satellite radar data and high space resolution images in the visible spectral range. Te developed method of iceberg detection is based on statistical criteria for fnding gradient zones in the analysis of two-dimensional felds of satellite images. Te algorithms of the iceberg detection, the procedure of the false target identifcation, and determination the horizontal dimensions of the icebergs and their location are described. Examples of iceberg detection using satellite information with high space resolution obtained from Sentinel-1 and Landsat-8 satellites are given. To assess the iceberg threat, we propose to use a model of their drif, one of the input parameters of which is the size of the detected objects. Tree possible situations of observation of icebergs are identifed, namely, the «status» state of objects: icebergs on open water; icebergs in drifing ice; and icebergs in the fast ice. At the same time, in each of these situations, the iceberg can be grounded, that prevents its moving. Specifc features of the iceberg monitoring at various «status» states of them are considered. Te «status» state of the iceberg is also taken into account when assessing the degree of danger of the detected object. Te use of iceberg detection techniques based on satellite radar data and visible range images is illustrated by results of monitoring the coastal areas of the Severnaya Zemlya archipelago. Te approaches proposed to detect icebergs from satellite data allow improving the quality and efciency of service for a wide number of users with ensuring the efciency and safety of Arctic navigation and activities on the Arctic shelf.

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