scholarly journals A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates

2021 ◽  
Vol 13 (14) ◽  
pp. 2693
Author(s):  
Na Zhao ◽  
Yimeng Jiao
Keyword(s):  
Spatial Resolution ◽  
High Accuracy ◽  
Surface Modeling ◽  
Scale Transformation ◽  
Monthly Precipitation ◽  
High Quality ◽  
Explanatory Variables ◽  
Downscaling Method ◽  
Precipitation Estimates ◽  
Global Precipitation

Obtaining high-quality precipitation datasets with a fine spatial resolution is of great importance for a variety of hydrological, meteorological and environmental applications. Satellite-based remote sensing can measure precipitation in large areas but suffers from inherent bias and relatively coarse resolutions. Based on the high accuracy surface modeling method (HASM), this study proposed a new downscaling method, the high accuracy surface modeling-based downscaling method (HASMD), to derive high-quality monthly precipitation estimates at a spatial resolution of 0.01° by downscaling the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation estimates in China. A scale transformation equation was introduced in HASMD, and the initial value was set by including the explanatory variables related to precipitation. The performance of HASMD was evaluated by comparing the results yielded by HASM and the combined method of HASM, Kriging, IDW and the geographical weighted regression (GWR) method (GWR-HASM, GWR-Kriging, GWR-IDW). Analysis results indicated that HASMD performed better than the other four methods. High agreement was achieved for HASMD, with bias values ranging from 0.07 to 0.29, root mean square error (RMSE) values ranging from 9.53 mm to 47.03 mm, and R2 values ranging from 0.75 to 0.96. Compared with the original IMERG precipitation products, the downscaling accuracy with HASMD improved up to 47%, 47%, and 14% according to bias, RMSE and R2, respectively. HASMD was able to capture the spatial variation in monthly precipitation in a vast region, and it might be potentially applicable for enhancing the spatial resolution and accuracy of remotely sensed precipitation data and facilitating their application at large scales.

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 Validation of CHIRPS Precipitation Estimates over Taiwan at Multiple Timescales

2021 ◽  
Vol 13 (2) ◽  
pp. 254 ◽  
Author(s):  
Jie Hsu ◽  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Xiuzhen Li
Keyword(s):  
Annual Cycle ◽  
Interannual Variation ◽  
Winter Precipitation ◽  
Monthly Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Precipitation Variation ◽  
Global Precipitation ◽  
Multiple Timescale ◽  
Better Than

The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which incorporates satellite imagery and in situ station information, is a new high-resolution long-term precipitation dataset available since 1981. This study aims to understand the performance of the latest version of CHIRPS in depicting the multiple timescale precipitation variation over Taiwan. The analysis is focused on examining whether CHIRPS is better than another satellite precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) final run (hereafter IMERG)—which is known to effectively capture the precipitation variation over Taiwan. We carried out the evaluations made for annual cycle, seasonal cycle, interannual variation, and daily variation during 2001–2019. Our results show that IMERG is slightly better than CHIRPS considering most of the features examined; however, CHIRPS performs better than that of IMERG in representing the (1) magnitude of the annual cycle of monthly precipitation climatology, (2) spatial distribution of the seasonal mean precipitation for all four seasons, (3) quantitative precipitation estimation of the interannual variation of area-averaged winter precipitation in Taiwan, and (4) occurrence frequency of the non-rainy grids in winter. Notably, despite the fact that CHIRPS is not better than IMERG for many examined features, CHIRPS can depict the temporal variation in precipitation over Taiwan on annual, seasonal, and interannual timescales with 95% significance. This highlights the potential use of CHIRPS in studying the multiple timescale variation in precipitation over Taiwan during the years 1981–2000, for which there are no data available in the IMERG database.

scholarly journals An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed

2021 ◽  
Vol 13 (2) ◽  
pp. 234
Author(s):  
Na Zhao
Keyword(s):  
High Resolution ◽  
Model Performance ◽  
Absolute Error ◽  
Rain Gauge ◽  
Monthly Precipitation ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Residual Correction ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit their applications. In this study, a downscaling scheme was developed to obtain precipitation estimates with high resolution and high accuracy in the Heihe watershed. Shannon’s entropy, together with a semi-variogram, was applied to establish the optimal precipitation station network. A combination of the random forest (RF) method and the residual correction approach with the established rain gauge network was applied to downscale monthly precipitation products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results indicated that the RF model showed little improvement in the accuracy of IMERG-based precipitation downscaling. Including residual modification could improve the results of the RF model. The mean absolute error (MAE) and root mean square error (RMSE) values decreased by 19% and 21%, respectively, after residual corrections were added to the RF approach. Moreover, we found that enough rain gauge records are necessary for and remain an important component of tuning model performance. The application of more rain gauges improves the performance of the combined RF and residual modification methods, with the MAE and RMSE values reduced by 8% and 9%, respectively. Residual correction, together with enough precipitation stations, can effectively enhance the quality of the precipitation patterns and magnitudes obtained in the RF downscaling process. The proposed downscaling scheme is an effective tool for increasing the accuracy and spatial resolution of precipitation fields in the Heihe watershed.

scholarly journals Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 333
Author(s):  
Altemar L. Pedreira Junior ◽  
Marcelo S. Biudes ◽  
Nadja G. Machado ◽  
George L. Vourlitis ◽  
Hatim M. E. Geli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Agricultural Production ◽  
Daily Precipitation ◽  
Annual Precipitation ◽  
Monthly Precipitation ◽  
Mato Grosso ◽  
Significant Difference ◽  
Precipitation Estimates ◽  
Global Precipitation

The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management.

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

2021 ◽  
Vol 13 (11) ◽  
pp. 2040
Author(s):  
Xin Yan ◽  
Hua Chen ◽  
Bingru Tian ◽  
Sheng Sheng ◽  
Jinxing Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Resolution ◽  
Daily Precipitation ◽  
Machine Learning Algorithms ◽  
Precipitation Data ◽  
Large Area ◽  
Tree Model ◽  
Daily Precipitation Data ◽  
Precipitation Estimates

High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.

Vertical precipitation profiles estimated by satellite-based passive microwave retrievals

2021 ◽  
Author(s):  
Nobuyuki Utsumi ◽  
F. Joseph Turk ◽  
Ziad. S. Haddad ◽  
Pierre-Emmanuel Kirstetter ◽  
Hyungjun Kim
Keyword(s):  
Water Content ◽  
Passive Microwave ◽  
Precipitation Rate ◽  
Vertical Profiles ◽  
High Latitudes ◽  
Surface Precipitation ◽  
Profile Information ◽  
Precipitation Estimates ◽  
Condensed Water ◽  
Global Precipitation

<p>Passive microwave (MW) observation from low Earth-orbiting satellites is one of the major sources of information for global precipitation monitoring. Although various precipitation retrieval techniques based on passive MW observation have been developed, most of them focus on estimating precipitation rate at near surface height. Vertical profile information of precipitation is meaningful for process-based understanding of precipitation systems. Also, a previous study found that the use of the vertical precipitation profile information can improve sub-hourly surface precipitation estimates (Utsumi et al., 2019).</p><p>This study investigates the precipitation vertical profiles estimated by two passive MW algorithms, i.e., the Emissivity Principal Components (EPC) algorithm developed by authors (Turk et al., 2018; Utsumi et al., 2021) and the Goddard Profiling Algorithm (GPROF). The vertical profiles of condensed water content estimated by the two passive MW algorithms for the Global Precipitation Measurement Microwave Imager (GMI) observations are validation with the GMI + Dual-frequency Precipitation Radar combined algorithm (CMB) for June 2014 – May 2015. The condensed water content profiles estimated by the passive MW algorithms show biases in their magnitude (i.e., EPC underestimates the magnitude by 20 – 50% in the middle-to-high latitudes; GPROF overestimates the magnitude by 20 – 50% in the middle-to-high latitudes and more than 50% overestimation in the tropics). On the other hand, the shapes of the profiles are reproduced well by the passive MW algorithms. The relationship between the estimation performances of surface precipitation rate and vertical profiles are also investigated. It is shown that the error in the profile magnitude shows a clear positive relationship with the surface precipitation error. The estimation performance of the profile shapes also shows connection with the surface precipitation error. This result indicates that physically reasonable connections between the surface precipitation estimate and its associated profiles are achieved to some extent by the passive MW algorithms. This also implies that properly constraining physical parameters of the precipitation profiles would lead to the improvements of the surface precipitation estimates.</p><p>References</p><p>Utsumi, N., Kim, H., Turk, F. J., & Haddad, Ziad. S. (2019). Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information. Journal of Hydrometeorology, 20(5), 1015–1026.</p><p>Turk, F. J., Haddad, Z. S., Kirstetter, P.-E., You, Y., & Ringerud, S. (2018). An observationally based method for stratifying a priori passive microwave observations in a Bayesian-based precipitation retrieval framework. Quarterly Journal of the Royal Meteorological Society, 144(S1), 145–164.</p><p>Utsumi, N., Turk, F. J., Haddad, Z. S., Kirstetter, P.-E., & Kim, H. (2021). Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals. Journal of Hydrometeorology, 22(1), 95–112.</p>

scholarly journals The Suitability of the Satellite Metrological Inputs Source on the Hydrological Model in a Small Urban Catchment

2016 ◽  
Author(s):  
Song Song ◽  
Youpeng Xu ◽  
Jiali Wang ◽  
Jinkang Du ◽  
Jianxin Zhang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Yangtze River Delta ◽  
Precipitation Data ◽  
Model Quality ◽  
Small Catchment ◽  
Explanatory Variables ◽  
Distributed Models ◽  
The Impact

Distributed/semi-distributed models are considered to be sensitive to the spatial resolution of the data input. In this paper, we take a small catchment in high urbanized Yangtze River Delta, Qinhuai catchment as study area, to analyze the impact of spatial resolution of precipitation and the potential evapotranspiration (PET) on the long-term runoff and flood runoff process. The data source includes the TRMM precipitation data, FEWS download PET data, and the interpolated metrological station data. GIS/RS technique was used to collect and pre-process the geographical, precipitation and PET series, which were then served as the input of CREST (Coupled Routing and Excess Storage) model to simulate the runoff process. The results clearly showed that, the CREST model is applicable to the Qinhuai catchment; the spatial resolution of precipitation had strong influence on the modelled runoff results and the metrological precipitation data cannot be substituted by the TRMM data in small catchment; the CREST model was not sensitive to the spatial resolution of the PET data, while the estimation fourmula of the PET data was correlated with the model quality. This paper focused on the small urbanized catchment, suggesting the influential explanatory variables for the model performance, and providing reliable reference for the study in similar area.

scholarly journals 3D ultrasound–guided resection of low-grade gliomas: principles and clinical examples

2019 ◽  
Vol 47 (6) ◽  
pp. E9 ◽  
Author(s):  
Geirmund Unsgård ◽  
Frank Lindseth
Keyword(s):  
3D Ultrasound ◽  
High Accuracy ◽  
Low Grade ◽  
Ultrasound Guided ◽  
High Quality ◽  
Case Examples ◽  
Low Grade Gliomas ◽  
Mesial Temporal Lobe ◽  
Temporal Lobe Lesions

3D ultrasound (US) is a convenient tool for guiding the resection of low-grade gliomas, seemingly without deterioration in patients’ quality of life. This article offers an update of the intraoperative workflow and the general principles behind the 3D US acquisition of high-quality images.The authors also provide case examples illustrating the technique in two small mesial temporal lobe lesions and in one insular glioma. Due to the ease of acquiring new images for navigation, the operations can be guided by updated image volumes throughout the entire course of surgery. The high accuracy offered by 3D US systems, based on nearly real-time images, allows for precise and safe resections. This is especially useful when an operation is performed through very narrow transcortical corridors.

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