scholarly journals GPM-Based Multitemporal Weighted Precipitation Analysis Using GPM_IMERGDF Product and ASTER DEM in EDBF Algorithm

2020 ◽  
Vol 12 (19) ◽  
pp. 3162 ◽  
Author(s):  
Sana Ullah ◽  
Zhengkang Zuo ◽  
Feizhou Zhang ◽  
Jianghua Zheng ◽  
Shifeng Huang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Thermal Emission ◽  
Empirical Distribution ◽  
Mainland China ◽  
Annual Precipitation ◽  
Coefficient Of Determination ◽  
Precipitation Variable ◽  
Precipitation Analysis ◽  
Global Precipitation Mission ◽  
Global Precipitation

To obtain the high-resolution multitemporal precipitation using spatial downscaling technique on a precipitation dataset may provide a better representation of the spatial variability of precipitation to be used for different purposes. In this research, a new downscaling methodology such as the global precipitation mission (GPM)-based multitemporal weighted precipitation analysis (GMWPA) at 0.05° resolution is developed and applied in the humid region of Mainland China by employing the GPM dataset at 0.1° and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m DEM-based geospatial predictors, i.e., elevation, longitude, and latitude in empirical distribution-based framework (EDBF) algorithm. The proposed methodology is a two-stepped process in which a scale-dependent regression analysis between each individual precipitation variable and the EDBF-based weighted precipitation with geospatial predictor(s), and to downscale the predicted multitemporal weighted precipitation at a refined scale is developed for the downscaling of GMWPA. While comparing results, it shows that the weighted precipitation outperformed all precipitation variables in terms of the coefficient of determination (R2) value, whereas they outperformed the annual precipitation variables and underperformed as compared to the seasonal and the monthly variables in terms of the calculated root mean square error (RMSE) value. Based on the achieved results, the weighted precipitation at the low-resolution (e.g., at 0.75° resolution) along-with the original resolution (e.g., at 0.1° resolution) is employed in the downscaling process to predict the average multitemporal precipitation, the annual total precipitation for the year 2001 and 2004, and the average annual precipitation (2001–2015) at 0.05° resolution, respectively. The downscaling approach resulting through proposed methodology captured the spatial patterns with greater accuracy at higher spatial resolution. This work showed that it is feasible to increase the spatial resolution of a precipitation variable(s) with greater accuracy on an annual basis or as an average from the multitemporal precipitation dataset using a geospatial predictor as the proxy of precipitation through the weighted precipitation in EDBF environment.

scholarly journals Assessment of Level-3 Gridded Global Precipitation Mission (GPM) Products Over Oceans

2019 ◽  
Vol 11 (3) ◽  
pp. 255 ◽  
Author(s):  
Sana Khan ◽  
Viviana Maggioni
Keyword(s):  
Performance Metrics ◽  
Probability Of Detection ◽  
The Other ◽  
Coefficient Of Determination ◽  
Dual Frequency ◽  
Level 3 ◽  
The Times ◽  
Global Precipitation Mission ◽  
Global Precipitation ◽  
Better Than

The performance of Level-3 gridded Global Precipitation Mission (GPM)-based precipitation products (IMERG, Integrated Multi-satellite Retrievals for GPM) is assessed against two references over oceans: the OceanRAIN dataset, derived from oceanic shipboard disdrometers, and a satellite-based radar product (the Level-3 Dual-frequency Precipitation Radar, 3DPRD). Daily IMERG products (early, late, final) and microwave-only (MW) and Infrared-only (IR) precipitation components are evaluated at four different spatial resolutions (0.5°, 1°, 2°, and 3°) during a 3-year study period (March 2014–February 2017). Their performance is assessed based on both categorical and continuous performance metrics, including correlation coefficient, probability of detection, success ratio, bias, and root mean square error (RMSE). A triple collocation analysis (TCA) is also presented to further investigate the performance of these satellite-based products. Overall, the IMERG products show an underestimation with respect to OceanRAIN. Rain events in OceanRAIN are correctly detected by all IMERG products ~80% of the times. IR estimates show relatively large errors and low correlations with OceanRAIN compared to the other products. On the other hand, the MW component performs better than other products in terms of both categorical and continuous statistics. TCA reveals that 3DPRD performs consistently better than OceanRAIN in terms of RMSE and coefficient of determination at all spatial resolutions. This work is part of a larger effort to validate GPM products over nontraditional regions such as oceans.

scholarly journals Statistical Properties of Global Precipitation in the NCEP GFS Model and TMPA Observations for Data Assimilation

2016 ◽  
Vol 144 (2) ◽  
pp. 663-679 ◽  
Author(s):  
Guo-Yuan Lien ◽  
Eugenia Kalnay ◽  
Takemasa Miyoshi ◽  
George J. Huffman
Keyword(s):  
Numerical Models ◽  
Companion Paper ◽  
Forecast Period ◽  
Marine Stratocumulus ◽  
Error Distributions ◽  
Precipitation Variable ◽  
Gfs Model ◽  
Precipitation Analysis ◽  
Non Gaussian ◽  
Global Precipitation

Abstract Assimilation of satellite precipitation data into numerical models presents several difficulties, with two of the most important being the non-Gaussian error distributions associated with precipitation, and large model and observation errors. As a result, improving the model forecast beyond a few hours by assimilating precipitation has been found to be difficult. To identify the challenges and propose practical solutions to assimilation of precipitation, statistics are calculated for global precipitation in a low-resolution NCEP Global Forecast System (GFS) model and the TRMM Multisatellite Precipitation Analysis (TMPA). The samples are constructed using the same model with the same forecast period, observation variables, and resolution as in the follow-on GFS/TMPA precipitation assimilation experiments presented in the companion paper. The statistical results indicate that the T62 and T126 GFS models generally have positive bias in precipitation compared to the TMPA observations, and that the simulation of the marine stratocumulus precipitation is not realistic in the T62 GFS model. It is necessary to apply to precipitation either the commonly used logarithm transformation or the newly proposed Gaussian transformation to obtain a better relationship between the model and observational precipitation. When the Gaussian transformations are separately applied to the model and observational precipitation, they serve as a bias correction that corrects the amplitude-dependent biases. In addition, using a spatially and/or temporally averaged precipitation variable, such as the 6-h accumulated precipitation, should be advantageous for precipitation assimilation.

Comparison of Precipitation Datasets over the Tropical South American and African Continents

2009 ◽  
Vol 10 (1) ◽  
pp. 289-299 ◽  
Author(s):  
Robinson I. Negrón Juárez ◽  
Wenhong Li ◽  
Rong Fu ◽  
Katia Fernandes ◽  
Andrea de Oliveira Cardoso
Keyword(s):  
Amazon Basin ◽  
Empirical Orthogonal Functions ◽  
Congo Basin ◽  
Annual Precipitation ◽  
Coefficient Of Determination ◽  
South American ◽  
Northeast Brazil ◽  
Orthogonal Functions ◽  
Precipitation Climatology ◽  
Global Precipitation

Abstract Six rainfall datasets are compared over the Amazon basin, Northeast Brazil, and the Congo basin. These datasets include three gauge-only precipitation products from the Climate Prediction Center (CPC), Global Precipitation Climatology Center (GPCC), and Brazilian Weather Forecast and Climate Studies Center (CLMNLS), and three combined gauge and satellite precipitation datasets from the CPC Merged Analysis of Precipitation (CMAP), Global Precipitation Climatology Project (GPCP), and Tropical Rainfall Measuring Mission (TRMM) product. The pattern of the annual precipitation is consistently represented by these data, despite the differences in methods and periods of averaging. Quantitatively, the differences in annual precipitation among these datasets are 5% more than the Amazon domain (0°–15°S, 50°–70°W), 22% more than Northeast Brazil (5°–10°S, 35°–45°W), and 11% more than the Congo domain (5°N–10°S, 15°–30°E). Over the Amazon domain the rainfall variation is well correlated between CPC, TRMM, GPCP, and GPCC (r 2 > 0.9) except for the northwestern Amazon, whereas CMAP and CLMNLS were different from these four datasets. Over the Congo basin, the coefficient of determination between these rainfall datasets is generally below 0.7. The empirical orthogonal functions analysis suggests large discrepancies in interannual and decadal variations of rainfall among these datasets, especially for the Congo basin and for the South American region after 1998. In general, CMAP, GPCC, TRMM, and GPCP significantly agree over the tropical areas in South America.

scholarly journals From TRMM to GPM: How Reliable Are Satellite-Based Precipitation Data across Nigeria?

2020 ◽  
Vol 12 (23) ◽  
pp. 3964
Author(s):  
Pius Nnamdi Nwachukwu ◽  
Frederic Satge ◽  
Samira El Yacoubi ◽  
Sebastien Pinel ◽  
Marie-Paule Bonnet
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
Annual Rainfall ◽  
Precipitation Data ◽  
Time Step ◽  
Satellite Precipitation ◽  
Satellite Retrievals ◽  
Precipitation Analysis ◽  
Global Precipitation Mission ◽  
Global Precipitation

In this study, 16 satellite-based precipitation products (SPPs) comprising satellite, gauge and reanalysis datasets were assessed on a monthly time step using precipitation data from 11 gauge stations across Nigeria within the 2000–2012 period as reference. Despite the ability of some of the SPPs to reproduce the salient north–south pattern of the annual rainfall field, the Kling–Gupta efficiency (KGE) results revealed substantial discrepancies among the SPP estimates. Generally, the SPP reliability varies spatially and temporally, with all SPPs performing better over part of central Nigeria during the dry season. When we compared the real-time and adjusted satellite-based products, the results showed that the adjusted products had a better KGE score. The assessment also showed that the reliability of integrated multi-satellite retrievals for Global Precipitation Mission (IMERG) products was consistent with that of their predecessor Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA). Finally, the best overall scores were obtained from multi-source weighted-ensemble precipitation (MSWEP) v.2.2 and IMERG-F v.6. Both products are therefore suggested for further hydrological studies.

scholarly journals Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

2021 ◽  
Vol 13 (3) ◽  
pp. 438
Author(s):  
Subrina Tahsin ◽  
Stephen C. Medeiros ◽  
Arvind Singh
Keyword(s):  
Vegetation Index ◽  
Coastal Wetland ◽  
Thermal Emission ◽  
Mean Squared Error ◽  
Wetland Vegetation ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

scholarly journals A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution

2021 ◽  
Vol 13 (11) ◽  
pp. 2211
Author(s):  
Shuo Xu ◽  
Jie Cheng ◽  
Quan Zhang
Keyword(s):  
Random Forest ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Learning Algorithm ◽  
Mainland China ◽  
Fusion Method ◽  
Moisture Condition

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.

scholarly journals A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 592
Author(s):  
Mehdi Aalijahan ◽  
Azra Khosravichenar
Keyword(s):  
Regional Planning ◽  
Interpolation Method ◽  
Absolute Error ◽  
Rain Gauge ◽  
Annual Precipitation ◽  
Coefficient Of Determination ◽  
Distance Weighting ◽  
Average Annual Precipitation ◽  
Inverse Distance

The spatial distribution of precipitation is one of the most important climatic variables used in geographic and environmental studies. However, when there is a lack of full coverage of meteorological stations, precipitation estimations are necessary to interpolate precipitation for larger areas. The purpose of this research was to find the best interpolation method for precipitation mapping in the partly densely populated Khorasan Razavi province of northeastern Iran. To achieve this, we compared five methods by applying average precipitation data from 97 rain gauge stations in that province for a period of 20 years (1994–2014): Inverse Distance Weighting, Radial Basis Functions (Completely Regularized Spline, Spline with Tension, Multiquadric, Inverse Multiquadric, Thin Plate Spline), Kriging (Simple, Ordinary, Universal), Co-Kriging (Simple, Ordinary, Universal) with an auxiliary elevation parameter, and non-linear Regression. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) were used to determine the best-performing method of precipitation interpolation. Our study shows that Ordinary Co-Kriging with an auxiliary elevation parameter was the best method for determining the distribution of annual precipitation for this region, showing the highest coefficient of determination of 0.46% between estimated and observed values. Therefore, the application of this method of precipitation mapping would form a mandatory base for regional planning and policy making in the arid to semi-arid Khorasan Razavi province during the future.

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