scholarly journals Calculation of rainfall from satellite data in and around Bangladesh

MAUSAM ◽  
2021 ◽  
Vol 51 (4) ◽  
pp. 359-364
Author(s):  
C. M. MUKAMMEL WAHID ◽  
MD. NAZRUL ISLAM ◽  
MD. REZAUR RAHMAN
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Precipitation Index ◽  
Horizontal Distance ◽  
Spatial Averaging ◽  
Temporal Scales ◽  
South Sea ◽  
Satellite Rainfall ◽  
Starting Location ◽  
Special Difference

We calculated GMS Precipitation Index (GPI, satellite rainfall) using three hourly IR data from GMS-5 over Bangladesh and adjoining areas for spatial resolution of 0.5° × 0.5°, l° × 1°,  2° × 2° and temporal scales of 1-day, 3-day and 7-day and monthly averages. There was no special difference between the spatial averaging scale of 0.5° or 1° mesh on land. The GPI contours were closely spaced in 0.5° mesh and better to comprehend the GPI fluctuation. From the monsoon  month of June to July the GPI maxima and minima shift from their original (starting) location. Both the GPI maxima and minima shifted toward north. There was an increase in GPI as one moved from north to south. Sea and offshore areas received almost uniform GPI compared to land areas where rain fluctuations occurred with little horizontal distance. It was found that actual rainfall was 88% of the GPI in this study.

scholarly journals Experiences in using the TMPA-3B42R satellite data to complement rain gauge measurements in the Ecuadorian coastal foothills

2013 ◽  
Vol 17 (7) ◽  
pp. 2905-2915 ◽  
Author(s):  
M. Arias-Hidalgo ◽  
B. Bhattacharya ◽  
A. E. Mynett ◽  
A. van Griensven
Keyword(s):  
Satellite Data ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
New Technologies ◽  
Simple Procedure ◽  
Rain Gauge ◽  
Experimental Methodology ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

Abstract. At present, new technologies are becoming available to extend the coverage of conventional meteorological datasets. An example is the TMPA-3B42R dataset (research – v6). The usefulness of this satellite rainfall product has been investigated in the hydrological modeling of the Vinces River catchment (Ecuadorian lowlands). The initial TMPA-3B42R information exhibited some features of the precipitation spatial pattern (e.g., decreasing southwards and westwards). It showed a remarkable bias compared to the ground-based rainfall values. Several time scales (annual, seasonal, monthly, etc.) were considered for bias correction. High correlations between the TMPA-3B42R and the rain gauge data were still found for the monthly resolution, and accordingly a bias correction at that level was performed. Bias correction factors were calculated, and, adopting a simple procedure, they were spatially distributed to enhance the satellite data. By means of rain gauge hyetographs, the bias-corrected monthly TMPA-3B42R data were disaggregated to daily resolution. These synthetic time series were inserted in a hydrological model to complement the available rain gauge data to assess the model performance. The results were quite comparable with those using only the rain gauge data. Although the model outcomes did not improve remarkably, the contribution of this experimental methodology was that, despite a high bias, the satellite rainfall data could still be corrected for use in rainfall-runoff modeling at catchment and daily level. In absence of rain gauge data, the approach may have the potential to provide useful data at scales larger than the present modeling resolution (e.g., monthly/basin).

scholarly journals EXTRACTING BUILT-UP FEATURES IN COMPLEX BIOPHYSICAL ENVIRONMENTS BY USING A LANDSAT BANDS RATIO

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 79-85
Author(s):  
A. H. Ngandam Mfondoum ◽  
P. G. Gbetkom ◽  
R. Cooper ◽  
S. Hakdaoui ◽  
M. B. Mansour Badamassi
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Experimental Site ◽  
Soil Features ◽  
Mixed Pixels ◽  
Shortwave Infrared ◽  
Normalization Process

Abstract. This paper addresses the remote sensing challenging field of urban mixed pixels on a medium spatial resolution satellite data. The tentatively named Normalized Difference Built-up and Surroundings Unmixing Index (NDBSUI) is proposed by using Landsat-8 Operational Land Imager (OLI) bands. It uses the Shortwave Infrared 2 (SWIR2) as the main wavelength, the SWIR1 with the red wavelengths, for the built-up extraction. A ratio is computed based on the normalization process and the application is made on six cities with different urban and environmental characteristics. The built-up of the experimental site of Yaoundé is extracted with an overall accuracy of 95.51% and a kappa coefficient of 0.90. The NDBSUI is validated over five other sites, chosen according to Cameroon’s bioclimatic zoning. The results are satisfactory for the cities of Yokadouma and Kumba in the bimodal and monomodal rainfall zones, where overall accuracies are up to 98.9% and 97.5%, with kappa coefficients of 0.88 and 0.94 respectively, although these values are close to those of three other indices. However, in the cities of Foumban, Ngaoundéré and Garoua, representing the western highlands, the high Guinea savannah and the Sudano-sahelian zones where built-up is more confused with soil features, overall accuracies of 97.06%, 95.29% and 74.86%, corresponding to 0.918, 0.89 and 0.42 kappa coefficients were recorded. Difference of accuracy with EBBI, NDBI and UI are up to 31.66%, confirming the NDBSUI efficiency to automate built-up extraction and unmixing from surrounding noises with less biases.

scholarly journals Use of multi-spectral visible and near-infrared satellite data for timely estimates of the Earth's surface reflectance in cloudy conditions: Part 2 - image restoration with HICO satellite data in overcast conditions

2021 ◽  
Author(s):  
Joanna Joiner ◽  
Zachary Fasnacht ◽  
Bo-Cai Gao ◽  
Wenhan Qin
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Precision Agriculture ◽  
Land Surface ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Space Station ◽  
Water Quality Assessment ◽  
Low Contrast

Satellite-based visible and near-infrared imaging of the Earth's surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using multi-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion ofthe panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment as well as disturbance, change, hazard, and disaster detection.

scholarly journals Transfer of Satellite Rainfall Uncertainty from Gauged to Ungauged Regions at Regional and Seasonal Time Scales

2010 ◽  
Vol 11 (6) ◽  
pp. 1263-1274 ◽  
Author(s):  
Ling Tang ◽  
Faisal Hossain ◽  
George J. Huffman
Keyword(s):  
Time Scales ◽  
Satellite Data ◽  
Time Scale ◽  
Spatial Interpolation ◽  
The United States ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Time Space ◽  
Satellite Rainfall ◽  
Transfer Accuracy

Abstract Hydrologists and other users need to know the uncertainty of the satellite rainfall datasets across the range of time–space scales over the whole domain of the dataset. Here, “uncertainty” refers to the general concept of the “deviation” of an estimate from the reference (or ground truth) where the deviation may be defined in multiple ways. This uncertainty information can provide insight to the user on the realistic limits of utility, such as hydrologic predictability, which can be achieved with these satellite rainfall datasets. However, satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite data will be most useful over regions that lack GV data, for example developing countries. This paper addresses the open issues for developing an appropriate uncertainty transfer scheme that can routinely estimate various uncertainty metrics across the globe by leveraging a combination of spatially dense GV data and temporally sparse surrogate (or proxy) GV data, such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and the Global Precipitation Measurement (GPM) mission dual-frequency precipitation radar. The TRMM Multisatellite Precipitation Analysis (TMPA) products over the United States spanning a record of 6 yr are used as a representative example of satellite rainfall. It is shown that there exists a quantifiable spatial structure in the uncertainty of satellite data for spatial interpolation. Probabilistic analysis of sampling offered by the existing constellation of passive microwave sensors indicate that transfer of uncertainty for hydrologic applications may be effective at daily time scales or higher during the GPM era. Finally, a commonly used spatial interpolation technique (kriging), which leverages the spatial correlation of estimation uncertainty, is assessed at climatologic, seasonal, monthly, and weekly time scales. It is found that the effectiveness of kriging is sensitive to the type of uncertainty metric, time scale of transfer, and the density of GV data within the transfer domain. Transfer accuracy is lowest at weekly time scales with the error doubling from monthly to weekly. However, at very low GV data density (<20% of the domain), the transfer accuracy is too low to show any distinction as a function of the time scale of transfer.

Remote Sensing of African Rainfall

2020 ◽  
Author(s):  
Tufa Dinku
Keyword(s):  
Climate Variability ◽  
Spatial Resolution ◽  
Rural Areas ◽  
Well Being ◽  
Early Warning Systems ◽  
Climate Data ◽  
Climate Variability And Change ◽  
Climate Services ◽  
Spatial Coverage ◽  
Satellite Rainfall

Climate data support a suite of scientific and socioeconomic activities that can reinforce development gains and improve the lives of those most vulnerable to climate variability and change. Historical and current weather and climate observations are essential for many activities, including operational meteorology, identifying extreme events and assessing associated risks, developing climate-informed early warning systems, planning, and research. Rainfall is the most widely available and used climate variable. Thus, measurement of rainfall is crucial to society’s well-being. In general, measurements from ground meteorological stations managed by National Meteorological Agencies are the principal sources of rainfall data. The main strength of the station observations is that they are assumed to give the “true” measurements of rainfall. However, the distribution of the meteorological observation network over Africa is significantly inadequate, with declining numbers of stations and poor data quality. This problem is compounded by the fact that the distribution of existing stations is uneven, with most weather stations located in cities and towns along major roads. As a result, coverage tends to be worse in rural areas, where livelihoods may be most vulnerable to climate variability and change. This has resulted in critical gaps in the provision of climate services where it is needed the most. Space-based measurements from satellites are being used as a complement to or in place of ground observations. Satellite-derived precipitation estimates offer good spatial coverage and improved temporal and spatial resolution, as well as near-real-time availability. Moreover, a range of satellite rainfall products are freely available from many sources, and a couple of these products are available only for Africa. However, satellite rainfall products also suffer from many shortcomings that include accuracy, particularly at higher temporal resolutions; coarse spatial resolution; short time series; and temporal inhomogeneity due to varying inputs. This limits the use of the use these products for certain applications. Understanding satellite rainfall estimation errors is critical for deciding which products might be used for specific applications and requires rigorous evaluation of these products using ground observations. The challenge in Africa is lack of availability, accessibility, and quality of rain-gauge observations that could be used for this purpose. Despite these challenges, there have been some validation efforts over different parts of the continent. However, different and inconsistent approaches of validation have created challenges to using these evaluation results. A comprehensive validation of the main operational satellite products at a continental level is needed to overcome these challenges and make the best use of satellite rainfall products in different applications.

scholarly journals Validation of Daily Rainfall Based on Global Satellite Mapping of Precipitation (GSMAP) Data of Bali and Nusa Tenggara Region

2018 ◽  
Vol 19 (1) ◽  
pp. 12
Author(s):  
Sanjaya Natadiredja ◽  
I Ketut Sukarasa ◽  
Gusti Ngurah Sutapa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Satellite Data ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Observation Data ◽  
Similar Data ◽  
Strong Positive Correlation ◽  
Cause Analysis ◽  
Satellite Rainfall

Limitations of observation data cause analysis and prediction of precipitation is difficult. One way to overcome such limitations is the use of satellite data such as GSMaP, but satellite data needs to be validated before use. This study aims to validate GSMaP rainfall data on observation data in Bali and Nusa Tenggara. Through monthly time series analysis, GSMaP rainfall data tend to have smaller value than observation data, but it has similar data pattern in each region with rain pattern that occurs in November to March (NDJFM). While validation between GSMaP satellite rainfall data and observation using Pearson and RMSE correlation and MBE at each location showed strong positive correlation value (> 0.5), correlation value obtained from each location from 0.82 to 0.93 with RMSE value from 2.08 to 5.51 and MBE values ??from 0.23 to 0.89, this indicates that GSMaP satellite data is valid and can be used to fill in empty data especially in 5 observation areas ie Denpasar, Ampenan, Sumbawa Besar, Bima and Kupang.

Sign in / Sign up

Export Citation Format

Share Document